A/B Testing

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:

  • Pages
  • Headlines
  • Subheadlines
  • Paragraph Text
  • Features
  • Tasks
  • Journeys
  • Micro Interactions
  • Macro Interactions
  • Forms
  • Conversion Flows
  • Conversion Objectives
  • CTAs
  • Ads
  • Banners
  • Images
  • Animations
  • Data
  • Dashboard
  • Action Panels

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