A simple example could be when a webpage is modified to create any changes made to it. This change can be as simple as a single headline or button, or be a complete redesign of the page.
Then, half of your traffic is shown the original version of the page (known as the control), and half are shown the modified version of the page (the variation). The engagement of users with both the classes of webpages is measured and collected in an analytics dashboard. Now, the changes in user experience, whether positive or negative, can be easily determined.
A/B testing allows minor and calculated changes to be made to the product without risking the user experience.
It can be used to target a single goal, i.e., improving user experience, improving conversion rate, etc.
One change at a time allows developers to understand what impact a particular change has on the user’s minds.
Marketers can test advertisements to learn which version and layout attract more clicks to make marketing campaigns more effective.
This method of introducing changes to a user experience also allows the experience to be optimized for the desired outcome. It can make crucial steps in a marketing campaign more effective.
The following is an adopted A/B testing framework used to run tests:
Collect Data: This is the key step, data collection and analytics lead a developer to provide some insight into changes that can be made.
Identify Goals: Your conversion goals are the metrics you are using to determine if the variation is more successful than the original version.
Generate Hypothesis: Generate testing ideas and hypotheses to incorporate structural and design changes to your version. List them and then prioritize them by difficulty and cost of implementation.
Create Variations: Make the changes using any A/B testing software.
Run Experiment: Perform the experiment where users can participate. Make sure they are given random access to control or variation. User interaction with each experience is measured, counted, and compared to determine how each performs.
Analyze Results: Analyze the results based on the data compiled by the A/B testing software.
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