What is an A/B test dashboard?
This particular type of dashboard tracks and measures A/B test results, presenting you with quality-based data in the context of quantity scales. The metrics that would typically feature on this dashboard give a look into Weighted Satisfaction Score and Positive Satisfaction Rating for asset A versus asset B. The A/B Test Dashboard monitors the results of A/B tests on specific features.
A/B Test dashboards are great for measuring whether variants in the product have made improvements or are actually to the detriment of the overall experience.
Our A/B test dashboard includes data from Mixpanel, exposing satisfaction data in a few different ways: through satisfaction improvement percentage, weighted percentage, and a bar chart to show the difference in each satisfaction score.
An A/B Test dashboard is a fantastic way to communicate some of the key metrics around these experiments to prove the value of your projects.
How we use the A/B Test UX Dashboard
In the UX team at Klipfolio, we’re constantly watching how people engage with the product and what their journey is to success or to abandonment. We use dashboards to measure changes to the product to see whether they have been an improvement or have been at the detriment of the overall experience.
For example, we use two visualizations, what we call Klips, to show the difference between the satisfaction ratings for the A Cohort versus that of the B Cohort. We communicate this through actual satisfaction improvement percentages, through a weighted percentage, and through a bar chart to more clearly show the difference in each of those satisfaction scores and how they relate. Because we run a lot of A/B tests, our SaaS dashboards allow us to communicate their progress and results within the UX team and to those outside of the team who are interested.
Metrics Featured in this Dashboard
- Weighted Satisfaction Score
- Positive Satisfaction Rating
- A/B Test Feature Funnels
- Edit to Save
- Edit to 6-month LTV
- Edit to Purchase
- Satisfaction Before and After Introduction
- Weighted Satisfaction
- Satisfied Responses
- Dissatisfied Responses
- Response Rates Not Affected
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