A/B testing, also known as split testing, is a method of experimentation used to compare two versions of an element (ie marketing content, website, landing page, etc.) to determine which version performs better. The process involves randomly assigning users or customers to either Variant A or Variant B and measuring their behaviors using predefined metrics such as click-through rate.
Experimentation such as A/B or multivariate testing is commonly used in ecommerce and digital marketing to optimize website pages, ads, marketing campaigns, and the overall customer journey. By comparing the performance of different variations of one element, the guess work is removed and marketers and products teams are able to make data-driven decisions when changing their marketing, site, and other content.
As a helpful tool in digital experience optimization, A/B testing can help ecommerce teams better understand their customers and ultimately help improve engagement and sales metrics such as click rates, conversion, AOV, and abandonment.
In marketing emails: A company wanting to improve the open rate of their email campaigns could create two versions of the same email with different subject lines, send each version to a randomly selected group of subscribers, and measure the open rate for each to see which performs better.
On landing pages: A website wanting to improve the conversion rate of their landing page could create two versions of the landing page with different designs, content, or calls to action, randomly direct traffic to each version, and measure the click-through or conversion rate for each to see which performs better.