A/B Testing and Conversion Rate Optimization
There are many ways to trace which business webpages or apps bring in the most traffic and sales. One way to compare user engagement on different webpages is through AB testing. In this blog, I will be discussing what AB testing is and why it’s an important method to understand according to Optimizely.com.
What is A/B testing?
AB testing compares two versions of a webpage intended to track how each page performs. First, a company collects data on its current webpage and then identifies goals and generates hypotheses using AB testing. To break it down, this method is an experiment where identical webpages, a control page (current user experience), and variations of the current control webpage, are shown to users at random. Website traffic is split where users land on different webpages. Visitor engagement data is tracked and collected on a dashboard then analyzed through a statistical engine. You can compare whether the change(s) to the webpage had positive, negative, or no effect in user behavior.
How does A/B testing work and how can it be useful?
AB testing can be used continuously to consistently improve user experience. AB is used to answer multiple questions and work towards a single goal or conversion goal. One goal a technology company might want to improve is sales and traffic from specific ad campaigns. In addition to the control webpage, this company would experiment with changing headlines, colors of an ad, SEO vocabulary, pop-ups, and more. Testing one change at a time gives a more specific read into what brings in more traffic and improves the user experience for a company to achieve its goals.
Marketers can learn which webpage version brings in more clicks. The click-through rate can be maximized and conversion rates can be improved when AB testing is applied to see which platforms produce the most clicks. An article by Neil Patel goes into further detail about optimizing the number of clicks: https://neilpatel.com/blog/what-is-the-optimum-number-of-clicks-before-conversions-start-happening/.
After receiving the results of your AB tests, you can see whether the learnings apply to other pages of your site. One can continuously experiment to generate new hypotheses and improve results on all pages of a business website. Comparing changes in user experiences while collecting data allows for teams and companies to best provide positive user experiences that increase the number of new and returning visitors as well as lead to more sales.
