A/B testing is a powerful method to determine which variations of your popups / widgets perform best. Poper makes it easy to conduct these tests and optimize your campaigns for maximum effectiveness.
Setting Up an A/B Test:
Access Your Poper Dashboard:
Log in to your Poper account and navigate to the main dashboard.
Open the "Campaigns" Tab:
Click on the "Campaigns" icon in the left-hand navigation menu.
Prepare Your Popups:
Ensure you have at least two Popers drafted within the same campaign. One will serve as the control (baseline), and the other(s) as variations.
Create a New A/B Test:
Click the "+ A/B Test" button at the top of the page.
Name Your Test:
Give your A/B test a descriptive name or description.
Select the Control Poper:
Choose the Poper you want to use as the baseline for your test. This is usually your existing or default design.
Add Variations:
Click "+ Add Variation" to select the other Poper(s) you want to test against the control. You can add multiple variations to a single A/B test.
Distribute Traffic:
Adjust the traffic distribution sliders to allocate traffic between the control and variation(s). It's recommended to avoid extreme splits (e.g., 80/20) as this can make it difficult to reach statistical significance.
Create the A/B Test:
Click "Create A/B Test." This will open a modal with additional settings.
Configure Test Details:
Test Duration: Choose how the test will end: by time (e.g., 24 hours, 1 week, 1 month) or by impressions (a combined total of impressions across all variations).
Action After Expiry: Decide what happens after the test concludes: keep the winner active and disable the rest, or disable all popups. The winner is determined based on a 95% confidence level.
Start the A/B Test:
After configuring the test details, click "Create A/B Test." This will add a card to your campaigns page representing the A/B test. Click the "Start" button to begin the test.
Monitoring and Analyzing Results:
Track Performance:
Poper will now randomly distribute traffic based on your defined distribution.
View Analytics:
Click the "Analytics" button on the A/B test card to access detailed performance data.
Analyze Metrics:
The analytics dashboard will display metrics such as impressions, conversions, conversion rate, uplift, and confidence level for each variation.
Confidence Level:
A confidence level below 95% will be highlighted in yellow, indicating that the results are not statistically significant. A green highlight indicates a confidence level of 95% or higher.
Determining a Winner:
Poper uses specific criteria to determine the winning variation:
Minimum Impressions: Each variation must have at least 30 impressions to be considered.
Higher Conversion Rate: The winning variation must have a higher conversion rate than the control popup.
Confidence Level: The confidence level must be at least 95%.
Stopping the Test:
You can manually stop the A/B test at any time by clicking the "Stop" button on the A/B test card.
By following these steps, you can effectively conduct A/B tests within Poper, gain valuable insights into your popup performance, and optimize your campaigns for maximum impact.