Imagine you want to know if pill A or pill B works better for a condition. You can’t test both pills on the same person because their body’s response might change over time or be affected by taking both pills. Instead, you need to test pill A on one person and pill B on another person who is similar in age, fitness, gender, etc. However, testing on just two people isn’t enough; you need to test many people to get accurate results.

The results will tell you which pill works better for people like the ones you tested. To know if the pills work for other types of people, you’ll need to plan tests for different groups, considering various factors. This shows how complex A/B testing can be: it’s not just comparing two options but doing it in a way that gives reliable and generalisable results.

Beyond Convention

A/B testing, also known as split testing, is a crucial strategy for marketers aiming to optimise their advertisements. In the context of Meta’s ad platform, which includes Facebook, Instagram, and other apps, A/B testing allows marketers to compare two versions of an ad to determine which performs better. This guide will help you understand the nuances of A/B testing, avoid common pitfalls, and effectively set up tests to maximise your ad performance. A/B testing traditionally involves comparing two versions of a single variable to identify which performs better. But A/B testing can and should be extended beyond this basic framework.

Example: Testing Audience Segments
Instead of just testing two ad creatives, consider testing different audience segments.

  • Version A: Target audience segment with affinity for “fitness and wellness.”
  • Version B: Target audience segment with affinity for “healthy eating.”

By doing so, you can determine not just which creative performs better, but which audience is more responsive to your ads.

Example: Testing Platforms
Test your ads across different Meta platforms (Facebook vs. Instagram).

  • Version A: Ad displayed on Facebook.
  • Version B: Ad displayed on Instagram.

Common Errors

Even experienced marketers can fall into traps that invalidate their A/B tests. Here are some common errors to avoid:

  • Insufficient Sample Size
    • Ensure your test runs long enough to gather sufficient data. Small sample sizes can lead to misleading results.
  • Running Multiple Tests Simultaneously
    • Running multiple A/B tests simultaneously can cause cross-test contamination, where the results of one test affect the results of another. To avoid this, use meta’s native A/B test function that limits cross contamination.
  • Changing Variables Mid-Test
    • Once a test is started, avoid making changes to the variables being tested. Any changes can skew the results and make the test unreliable.
  • Focusing Solely on CTR (Click-Through Rate)
    • While CTR is important, it’s crucial to consider other metrics such as conversion rate, cost per conversion, and ROI to get a holistic view of ad performance.

Reporting Dimensions

When setting up your A/B test, the dimensions you report on can significantly impact the insights you gain.

Example: Geographic Performance
Reporting on geographic dimensions can reveal location-based preferences.

  • Version A: Ad performance in urban areas.
  • Version B: Ad performance in rural areas.

Example: Device Performance
Understanding how your ad performs on different devices can optimise budget allocation.

  • Version A: Ad performance on mobile devices.
  • Version B: Ad performance on desktop devices.

Remember that what dimensions you want to report is what you should setup your A/B test for.

Isolating Variables

To obtain accurate results, it’s vital to limit and isolate variables effectively.

Isolate One Variable at a Time

If you’re testing ad copy, keep the visuals constant.

Example: Testing headlines.

  • Version A: “Discover the Best Fitness Gear!”
  • Version B: “Upgrade Your Workout Today!”

There is absolutely no point testing large changes in visuals or copy because your results will always be inconclusive.


    Effective A/B testing on Meta’s ad platform requires a strategic approach beyond conventional methods. By considering various factors like audience segments, platforms, reporting dimensions, and isolating variables, marketers can gain deeper insights and make data-driven decisions to optimise their ad performance. Avoid common pitfalls, and remember to run tests long enough to gather significant data, ensuring the validity of your results.