Articles on: PERFORMANCE AND ANALYSIS

Focus A/B Test - Understanding and analyzing your campaign

The purpose of running an A/B test campaign is to analyze and compare the performance of multiple creatives for the same campaign objective. This helps you quickly identify what works best and optimize your campaigns more effectively.

In this article, we explain where to find your results, how to read the metrics, and how to interpret them.

How can I see my campaign results?

To view your results, simply click on the campaign from:

your global dashboard,
or the artist dashboard.

Inside the campaign view, you will find three main sections:

Metrics: This section shows the overall campaign results (spend, results, costs, etc.).

Performance: Here, you can analyze performance by audience and by country.

A/B Test: This is the key section for A/B test campaigns, where you can see results per creative variant**.

How to understand A/B Test results?

Inside the A/B Test section, there are three main parts:

1. Variant Performance

This graph shows how each variant’s performance evolves over time.

You can choose to display:

the Key Result, or
the cost per Key Result.,
number of impressions,
spend

(The key result depends on the type of campaign you launch)

This helps you clearly identify which creative performs best and how performance changes throughout the campaign.

2. Best Performance

This is the most important section.

Our algorithm automatically identifies the best-performing variant and highlights it here.

You will see:

the winning variant,
as well as some of the most important key indicators for understanding why this content performed better.

3. Variant status overview

This final section shows all variants with their status and key metrics.

As with other campaigns, our algorithm will automatically stop the least performant variants and focus the budget on the best one.

TIP : You can also check out this article, which can help you understand the different statistics and analyze performance in general.

What lessons can be learned from A/B testing: When the campaign comes to an end, several scenarios are possible:Since the algorithm automatically optimizes the campaign, you can simply let it run.You can also add more budget or extend the campaign duration to maximize the results of the winning variant.Once the campaign ends, you can create a new campaign using only the best-performing creative.

Updated on: 13/01/2026

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