Introduction to A/B testing

How can A/B testing be useful for your organisation?

For government, political and non-profit organisations, aligning measurement approaches with strategic goals and KPIs is essential. A/B testing helps you:

• Focus on what matters: Tests are tied to real KPIs, measuring changes that support your advertising objectives.
• Make data-driven decisions: Tests provide quantitative evidence rather than relying on hunches or anecdotal feedback.
• Balance speed and rigour: You can get quick feedback while maintaining statistical confidence.
• Gain insights before launch: You can safely experiment with new approaches before running full campaigns.
• Easily run tests: It's well suited for all government, political and non-profit organisations with any amount of measurement experience, whether the campaign is large or small, and tests are easily set up in Ads Manager or the Experiments tool.

A/B testing can enable your team to move confidently and strategically towards better outcomes, staying aligned with what matters most.

How does A/B testing compare to Meta's other measurement tools?

It's important to choose the right measurement tool for your question or goal. Here's a comparison of the main Meta tools:

• A/B testing: Compares versions of an ad strategy by changing a single variable (creative, copy, targeting) then evaluates the direct impact on a KPI. Best for rapid iteration and tactical optimisation.
• Conversion Lift: Measures the incremental lift – the true causal effect – of ads on conversions versus a control group. Ideal for quantifying actual impact.
• Brand Lift: Measures the incremental lift in changes in perception, recall or attitudes based on poll responses rather than conversions.

A/B testing can be used for direct, controlled comparisons, while lift tests are more relevant when your goal is to understand causal, incremental campaign impact.

Best practices for A/B testing

To get meaningful and actionable results, follow these guidelines:

• Start with a hypothesis: Define a measurable hypothesis refined into a specific outcome.
• Test one variable at a time: Cells should be identical except for the variable that you're testing (e.g. creative or placements), including using the same budget for both versions to ensure a fair comparison. This allows you to understand that the results were specifically caused by that variable and you can take further strategic action with confidence. If you have more than one variable to test, change only one element per test, and plan to run multiple tests.
• Use a large enough audience and budget: Ensure that groups are big enough for statistical confidence, and include a budget that will produce enough results to confidently determine a winning strategy. Your campaigns must reach enough people so that enough people are taking the action that you are measuring for the system to ultimately determine a winning cell.
• Run the test for at least 7 days: A/B tests can only be run for a maximum of 30 days, but tests shorter than 7 days may produce inconclusive results. This accounts for variability and improves reliability.
• Align your test with your organisation's KPIs: Avoid proxy metrics! Just as it is recommended to align your campaign objective with the real-world outcome that you are trying to drive, it is recommended to choose a measurable outcome that is directly tied to your goal for the campaign.