Jeff Bezos famously said, “Our success is a function of how many experiments we do per year, per month, per week, per day.” Why use split testing? Google conducted 17,523 live traffic experiments, resulting in 3,620 launches in 2019.

Netflix is notorious for running experiments on their sign-up process and content effectiveness, and they encourage their designers to think like scientists. Production releases don’t happen at LinkedIn without split testing.

Experimentation is so integral to some businesses that they developed their own customized tools for their testing needs. Some of the most innovative companies in the world rely on A/B tests for marketing and product decisions. If you’re testing a blue button against a yellow button split evenly among your audience, and testing reveals that the button should indeed be blue, the risk of exposing the less effective experience is cut by half. The financial and opportunity cost of making the wrong decision can be minimized with A/B testing, not to mention that the learning gained during the test can be invaluable. Testing, rather than guessing, yields valuable time for creative teams, marketing teams, and operational associates to work on other priorities.

Supported by data, the right decision can become apparent after about a week of recorded outcomes. Good testing requires thoughtful planning and execution of the test so that the data collected can be trusted and analyzed to form objective and valid conclusions.Ī/B tests can improve operational efficiency. It’s a scientific approach to achieve a certain goal. You can A/B test subject lines of an email to try to boost open rates. Warning: Don’t let your digital marketing fall victim to HiPPO (Highest Paid Person’s Opinion syndrome).Ĭonducted across numerous marketing channels, you can A/B test messaging, images, colors, or objects even more complex, such as form fields and user flow on a website. Instead of spending valuable meeting time debating what color the button should be (who doesn’t love that, right?), split testing helps facilitate the conversation to focus more on the data, rather than opinion or emotion. It removes the guesswork from decision making and lets the data decide the path forward. A/B testing allows any organization to be more data-driven and strategic about their digital communications.

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