What is A/B testing? Boost Your Conversion Rates

3 years ago -

What is A/B testing? A/B testing allows you to test different campaign variables to see which elements work best to achieve your campaign goals. It involves randomly showing each version to a subset of users and measuring their interactions to see which one achieves the desired outcome.
Facebook’s A/B testing tools work across the Facebook family of apps and services to help you understand which advertising strategies have the greatest impact on your campaign performance. Insights from A/B testing can be used to improve future advertising plans.

What is A/B testing?

What is A/B testing? A/B testing, also known as split testing, allows you to test different campaign variables (creatives, delivery optimization, audiences, placements) to see which elements work best to achieve your campaign goals.

It involves randomly showing each version to a subset of users and measuring their interactions to see which one achieves the desired outcome.

How does A/B testing work?

Facebook’s A/B testing tools make it easy to test different ads to see which ones work best. A/B testing divides your audience into random, non-overlapping groups. These groups show identical sets of ads in all respects except for one distinct difference: the ‘variable’.

For example, variables could be different creatives, audiences, placements, or delivery optimizations.
Each ad set’s performance is then measured against your campaign goals, and the best-performing ad set is chosen. Once the A/B tests are complete, you will receive an email and notification of your winning strategy.

A/B test lets you test one or more variables, such as theme, audience, delivery optimization, placement, and more.

Creative variables: This allows you to compare the performance of different ad images, copy, or creative types. For example, tests can be used to compare the performance of ads using video assets and image carousels. Another test might show ads with the same image or video but different text. Choosing an audience variable allows you to compare the effectiveness of your ads in reaching different audiences and demographics. For example, you can add or remove specific regions from your audience, or compare your Custom Audience to your Core Audience.

Delivery Optimization variable: This is use variable delivery optimization to test which optimization works better. In A/B delivery optimization, you can run two of your ads that drive visitors to the website. One optimizes ad serving for impressions and the other for link clicks. You can then track your metrics to see which ads are performing better.

Placement variable: This is used placement variables to compare how effectively different placement types reach your audience. For example, you can compare automatic placements with specially selected ad placements. Alternatively, you can compare your Facebook ranking with your Instagram ranking.

A/B testing is a powerful tool for increasing conversion rates. By comparing two versions of a webpage or marketing campaign, you can determine which one performs better and make data-driven decisions to improve results. Avoid these mistakes while using A/B Testing on Facebook.

How to use Facebook A/B test?

You can create an A/B test by selecting a campaign or ad set in Ads Manager and clicking the button labeled “A/B Testing”.

Select a campaign objective that enables A/B Test: Brand Awareness, Reach, Engagement, Traffic, Video Views, Lead Generation, App Installs, Conversions, Catalog sales, and Messages.
Select the option to create an A/B test.
Select the variable(s) to test, i.e., Creative, Audience, Delivery Optimization, or Placements.
Run your campaign.
Once a winner has been determined, you’ll be able to determine your winning ad set in Ads Manager, and an email will be sent to you with the results.

About Author

Ruby is an IT Engineer | Blogger | Digital marketer who offers Digital Marketing Services, Content Creation, and Blogging services to help brand that gains social media attention and increases search engine visibility.

Ruby Saud View all post from the Author