What is A/B Testing?
A/B testing is a simple yet crucial practice for marketers who want to optimize their digital or marketing strategy. By creating and comparing two versions of any asset, designers can discover which content appeals most to their audience and learn a lot about their customers along the way.
When to use A/B Testing
A/B testing can be used to test nearly everything from landing pages, to display ads, to marketing content.
Why is A/B Testing important?
Instead of relying on your intuition when it comes to making decisions, A/B testing produces the data to help you understand which strategy would lead to the most clicks or conversions. The results will provide you with insights to help you make the most out of your budget and maximize your return on investment.
How to run an A/B Test
Step 1: Identify your goals
Before starting your A/B test, it is crucial to identify your problem area which will help you establish your goals. Be specific about exactly which customer actions you hope to drive. Is it to generate more clicks? Or is it to increase more purchases? Your goals will be used to determine the metrics you will focus on throughout the project.
Step 2: Create Hypothesis
Once you’ve identified your goals, you can start generating hypotheses for your A/B testing. Keep in mind to list down ideas of what could perform better than the current version.
You can even develop data-backed hypotheses by doing research related to your business goals to ensure that you are on the right track.
Step 3: Create Variation
The next step is to create variation based on your selected hypothesis. The variation is simply the desired changes to an element of your existing version. For example, if your goal is to identify a new subject line to increase your email open rate, the original subject line will serve as the existing version while the new subject line will serve as your variation.
Step 4: Run experiment
When running your A/B test, keep in mind to run the test under the same condition to minimize factors that could interfere with your results. For example, the seasonality or the timing can skew your test results. To ensure statistical accuracy, users should be randomly assigned to either the control or variation of your experiment. Traditionally, it’s believed that the minimum duration of your test should last for two weeks to receive as much sample as possible. However, with modern sampling tools like FC Panels, you can get thousands of responses in just hours. Plus, running a test for too long can be counterproductive since there are more external variables you cannot control over a longer period of time.
Step 5: Analyze your results
Now you can draw insightful conclusions based on your campaign winner! Be sure to analyze your test results by considering metrics such as percentage increase, confidence level, or direct and indirect impact on other metrics. These numbers will provide a clear threshold to help you define the winner of your experiment. If neither variation is statistically better, you can mark the test as inconclusive and rerun the test. Overall, you should be able to gain a deeper understand what your audience preferred and by what margin.
Limitations with A/B Testing
What worked today might not work a month from now. Each A/B test has a duration because user behavior and expectations are always changing. Today, the variation might be the winner of your campaign because of its novelty, but it might lose its value after a few months if your competition rolls out something similar. Make sure to run A/B tests frequently to ensure your product experiences are performing as well as you think they are.
There is a built-in assumption that all tests are independent of each other, which can be false at times. Each campaign winner from different A/B tests can yield amazing results separately, but terrible together. For example, one team finalizes to introduce abandoned cart notifications while the other conclude to send product recommendations to consumers. By implementing both strategies simultaneously, your business metrics tank because consumers become too annoyed from receiving too many emails.
A/B testing is one of the most powerful ways to gain insight into your audience’s preferences. Each test is a step closer to understanding your customer’s needs. No matter how satisfied you are with your current performance, there is always room for improvement! Be sure to run your test continuously and avoid any pitfalls.