A/B testing is also known as ‘split testing’. Experimenters aim to discover whether changing specific elements can influence the performance of a website, app, advert or email campaign against a goal. From studying user behavior (for example, website data), experimenters devise a hypothesis to test. They then produce a variation of a targeted element. Users are split between the ‘control’ (or original) and variant simultaneously and left to interact with the product over a set period of time. Almost anything on your website, app or campaign can be tested, but some example includes:
a) Headlines
b) Subheadings
c) Paragraph Text
d) CTAs or Call To Actions
e) Images
Following a close examination of how the user engages with the new design, experimenters use statistical analysis to determine which version (the control or the new) performs best against the pre-set goal.
They usually focus on the overall conversion rate. For instance, experimenters could change the color or layout of a certain element and test whether this has a positive, negative or no effect on visitor behavior and conversion rates.
If a user’s engagement with a variant is high, and the change boasts an excellent or improved conversion rate, experimenters select the version with higher performance.
They use it for future iterations of a campaign (for example, emails) or implement it on a web page or app. A/B testing tools often have calculators to help experimenters ensure results are statistically significant and test are not being concluded too early.
Through close, data-driven analysis, A/B testing removes the guesswork from website optimization and ensures that changes yield positive results.
The measure of the time required for a solid test will differ contingent upon factors like your change rates, and how much movement your site gets; a great testing apparatus should disclose to you when you’ve sufficiently accumulated information to reach a dependable determination. Once you’ve closed the test, you should refresh your site with the coveted substance variation(s) and evaluate all components of the test at the earliest opportunity, for example, exchange URLs or testing contents and markup.
