A/B Testing experimentation is one of the best UX validation methods to test and validate variant assumptions on whether the differences influence the performance of a web application, feature, product, service...vs targeted goals. A/B Testing also known as "slipt testing".
A/B testing is a way to compare two versions of a single variable, in most cases by testing a subject's response to variant A against variant B, validating with evidence which of the two variants is more effective. A/B Tests aim to study user behaviour.
Almost anything can be tested, including:
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After a close examination of how users interact and engage with the variants A/B of the testing, and using statistical analysis tools, can help to determine which of the variant has performed best for the specific pre-set goal. The focus can be on the overall or specific conversion rate of the goal to be achieved.
If a user's engagement with a variant is higher than the other variant, and the differences boost and improve conversion rates. The test provides evidence enough to select the variant that performs best.
Data-driven analysis, A/B testing is one of the best testing methods to gather evidence for the best performance of the same designs but with different variants.
Recent case studies have proven that A/B Testing method for experiment with variants helps businesses to increase revenue.
A/B Case Studies:
- HP Experiment - $21 million
Incremental revenue impact - MISSGUIDED Experiment - 177% Conversion uplift, 33% revenue
Incremental revenue impact
A/B Testing Tools