A/B Testing
Compare two or more unique web experiences side-by-side to determine which one performs best with your audience.
What is an A/B Test?
A/B Testing allows you to compare two or more unique funnels side-by-side to determine which one performs best with your audience. Whether you’re testing pricing, messaging, step order, or visual layout—experiments help you optimize every part of your funnel with real data.
You can use A/B tests to:
Test different price points
Try various question flows
Showcase different features
Experiment with design changes, value propositions, and more
All funnels in a test are shown to the same target audience, helping you pinpoint what converts best.
Purpose and Benefits
Experiments are designed to help you:
Find the highest-converting funnel
Identify strong-performing pages and eliminate weak ones
Discover optimal pricing your users are willing to pay
Test fast and iterate confidently
Make data-driven decisions based on real user behavior
Creating an A/B Test
To create a new experiment:
Navigate to the A/B Test tab in the main dashboard
You'll see a table with all your experiments and basic performance metrics
To view more detailed data, click on any active experiment
Create a New Experiment
Click "Create Experiment" in the top-right corner
In the creation flow:
Give your experiment a name
Assign funnels to the experiment
Once your funnels are added, you can:
Adjust the distribution of visits across funnels
By default, traffic is split evenly, but you can customize it as needed
The percentage determines how much traffic each funnel receives
Sharing the Experiment Link
After creating an experiment:
Open the experiment from the table view
Click "Copy A/B Test Link" in the top-right corner
This is the production link to your experiment
Use this link in ad campaigns or standalone—wherever you want to drive traffic to the test
Analyzing Experiment Results
Once your A/B Test is live, you’ll be able to monitor rich performance data:
Total Revenue from the experiment
Number of experiment visits
Number of new customers generated
Average conversion rate across all funnels
Best performing funnel based on performance
Per-page conversion rates within each funnel
Per-funnel conversion rate breakdown
Editing Active Experiments
You can edit an experiment on the fly, including changing traffic distribution or replacing funnels.
Editing active experiments may skew your data.
For best results and clean analytics, it's strongly recommended to create a new experiment instead.
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