{"id":45430,"date":"2021-02-26T10:00:31","date_gmt":"2021-02-26T17:00:31","guid":{"rendered":"https:\/\/www.clearvoice.com\/resources\/what-is-ab-testing\/"},"modified":"2024-02-28T10:29:10","modified_gmt":"2024-02-28T17:29:10","slug":"what-is-ab-testing","status":"publish","type":"post","link":"https:\/\/www.clearvoice.com\/resources\/what-is-ab-testing\/","title":{"rendered":"A\/B Testing"},"content":{"rendered":"

What is A\/B testing?<\/h2>\n

A\/B testing is a simple yet incredibly effective research tool where two variations of an idea are released into the marketplace to see which method is the most effective. Also known as split testing, this method is used to refine headlines, subject lines, emails, web pages, advertisements, or any other content to determine which version performs better in achieving a specific goal. By testing different versions simultaneously, we take the guesswork out of what content resonates with our audience.<\/p>\n

\"Why<\/p>\n

Why is A\/B testing so important?<\/h2>\n

Our instincts can make us great content marketers, but our gut ideas will only get us so far \u2014 real-world\u00a0marketing strategy<\/a>\u00a0results from trial and error, and, most importantly, testing.\u00a0A\/B testing allows us to design experiments for our audience, where we can learn what truly inspires them to take action. Our decisions are then made on objective data rather than subjective opinions or assumptions.<\/p>\n

How do we get started?<\/h2>\n

While A\/B testing is a simple methodology, being effective takes a little more thought and preparation.<\/p>\n

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  1. Define your objective:<\/strong>\u00a0What are you trying to improve or learn about your audience? A campaign designed to increase click-through rates may look different than one focused on converting new clients.<\/li>\n
  2. Identify the variable:<\/strong>\u00a0Experiments are only effective if they test one data point at a time. Choose one specific element of your content that you want to test. It could be a headline, call-to-action (CTA), layout, color scheme, or any other component that can be altered.<\/li>\n
  3. Create two versions:<\/strong>\u00a0This is where the \u201cA\u201d and \u201cB\u201d in A\/B testing make their debut. Like all experiments, we will have a control or original design, version A. Version B will incorporate the theories or variations we want to test.<\/li>\n
  4. Split your audience:<\/strong>\u00a0Divide your target audience\u00a0randomly\u00a0into two groups. Group A will be exposed to version A, and Group B will be exposed to version B. Making the groups random prevents you from accidentally influencing the data through subconscious assumptions about how the audience will behave.<\/li>\n
  5. Measure and compare:<\/strong>\u00a0Track the performance metrics of both versions using relevant analytics tools. This could include click-through rates, conversion rates, time spent on a page, or any other metric that aligns with your objective.<\/li>\n
  6. Analyze the results:<\/strong>\u00a0Compare the performance of A and B to identify the better version. Focus only on statistically significant differences in the metrics between the two groups.<\/li>\n
  7. Iterate and optimize:<\/strong>\u00a0A\/B testing is an ongoing process. Use the insights gained from each test to inform future iterations and continuously refine your content marketing strategies.<\/li>\n<\/ol>\n

    \"Using<\/p>\n

    Using A\/B Testing to Mitigate Risk<\/h2>\n

    Even the most basic content can be made up of thousands of little decisions. How do we know which one isn\u2019t working? We could have killer copy, but the graphics feel confusing. Our CTA\u2019s may be effective on the website but lose their luster on socials. We could have the best product in the world, but it\u2019s just in the wrong color!<\/p>\n

    Our frustrations can cause us to throw the baby out with the bath water, or we can use \u00a0A\/B testing to pull apart, reorganize, and test various factors in our content until we determine what aspect is falling short. The ability to pinpoint your pain points saves time and money, preventing long phases of internal ideation.<\/p>\n

    The ability to reduce risk is also a major selling point when acquiring new clients. Instead of making changes based on hunches or preferences, marketers who implement regular A\/B testing can rely on concrete data to guide their decision-making. This prevents implementing ineffective changes and ensures that content alterations are backed by evidence, not hunches.<\/p>\n

    A\/B testing allows us to allocate our resources better. By testing and refining content, we can focus more on the efforts and investments that yield the best results. This reduces wasted resources and drastically improves the ROI of our campaigns. A\/B testing fosters a culture of learning and adaptation, which will always lead to better content performance over time.<\/p>\n

    A Real-life Example of A\/B Testing<\/h2>\n

    Usually, A\/B testing exceeds two options and considers various segments of current, potential, or past customers.\u00a0Take, for example, a client sharing the news of an upcoming sale via\u00a0their newsletter<\/a>. It will go out to a list of the following people:<\/p>\n