Achieving Conversion Rate Optimization (CRO) through A/B Testing

Conversion rate optimization is exactly how it sounds. It is optimizing the conversion rate for your web pages. This can be done using a variety of different tools but here we will talk about A/B testing for optimizing conversion rate. To achieve CRO, we are going to need to make many changes to our pages that will increase micro and macro-conversions and we will measure the impacts of these changes through A/B tests.

In an A/B test, a group of users is shown a controlled version of a page and another group is shown a different version of the page. All metrics are closely tracked but we are specifically looking at how the users interact with these pages and are compared to choose the optimal version of the page that is most likely to achieve the goal. If we are trying to drive micro-conversions, linking pages to others, we may track metrics like the bounce rate, average time on page, click-through rate, pageviews, and new visitors. There are two requirements for A/B tests to be successful and the first is to test only one variable at a time. If you change multiple variables between the compared groups, it will be nearly impossible to tell which changes contributed to the difference in conversions. The second requirement is to test with evenly distributed groups. If they are disproportionate, standardizing or shaving down the data will not give you accurate results so the groups must be even.

When done correctly, A/B testing would be used to choose the optimal pages and setups to drive those micro-conversions into a macro-conversion. It can also be used by advertisers to choose which places are best for which advertisements. Facebook has integrated A/B testing tools for tracking the analytics of your advertisements. Here, you can easily choose which campaigns you want to compare over time, and which variables you want to track within those campaigns. For more information on different A/B test variables, check out this HubSpot article where they detail variables like calls-to-action, copies, and forms. Google is a leading example of a company that utilizes A/B testing to drive conversions. In their tests, they aimed to increase the quality of search results and user experience. They have done this well by changing the look and feel of their search engine many times in the last decade.