A/B Testing
A/B Testing also known as Bucket testing or Split Run Testing is a research methodology used to evaluate user experience. It is widely used by e-commerce and social media websites as well as popular tech giants to analyse versions that works better.
It was first used in 1835 for clinical trials to find the effectiveness of a homeopathic drug. But later with the growth of internet, this experimentation method which was mainly used offline became rampantly used online to analyse customer preferences and behaviours.
During early twentieth century, it was used by advertising pioneer Claude Hopkins to analyse the effectiveness of an advertising campaign. Towards the end of 20th century its use was rampant among various tech giants to test different variants among its customers. One such example is when pioneer tech giant in 2000 used this method to determine the optimum number of displays on its search engine.
A/B testing can be defined as a randomized controlled experimentation method to determine which variation among variation A and variation B (assumed values) works better. The variation mentioned here can be the size of select button in a website, the colour of an icon, the layout of an e-commerce website, the analysis of better discount offers, acceptance of better deals in an e-commerce website or can even be the analysis of better size and shape of subscription button.
In 2009, a leading tech giant used this research method to decide shades used for advertising links used in its website. So, the website tested 41 shades among 1% of its customers. A single colour received the maximum clicks, that helped in generating $200 million heave in its ad revenue.
Other areas where this experiment method used in customer preferences, email marketing, product pricing, in analysing effectiveness of political campaigns, etc.
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