As a B2B marketer, A/B testing is key to making smart, effective, data-driven decisions. The usual process is to split test variations of a marketing element on two randomly and equally-divided audience segments. Based on a statistical analysis of the results, you can optimize your strategy one step at a time, to eventually increase conversion rate, boost engagement and minimize overall risk.
With this in mind, here are 7 guidelines that will help you A/B test correctly, and make the most of this tool.
Do test every possible marketing element
There are bound to be numerous elements in your digital marketing strategy. Test every element, from social media and website content to preview content, ads, CTAs, ad formats, and more. When you test multiple variations of each element, you will be able to refine all communication to the best of your ability, making it as impactful as possible.
Don’t test all elements in one go
While you must identify which elements you’re going to test, never run the experiment on all of them simultaneously, as it is nearly impossible to get an accurate picture. For instance, let’s assume that you have created two variations of a landing page, with different graphics on each. You have also placed different CTAs on each landing page. Since there are two variables, you’ll have no sure way of knowing whether one version is outperforming the other owing to the difference in graphics or the CTA. Split testing is pure science, and there shouldn’t be room for guesswork.
Do ensure your audience segments are similar
The audience that you use for testing must have similar traits for accurate, comparable results. If not, different behavioral patterns will give rise to unreliable results. Once you have ensured this, randomly divide the group into two equal-sized segments and begin testing.
Don’t test non-critical elements
If any piece of communication is a non-essential one-off, don’t waste your time on an A/B test. Here’s a closer look at what this means: Let’s assume that you have fun events planned every Christmas. Now, communication pertaining to this is not directly business-related, but aimed at boosting employee morale, and showcasing your organization in a positive light. As it is a non-critical activity, you don’t need to test the communication here. Prioritize on testing elements and pieces of communication that can improve sales, revenue, or even discoverability.
Do analyze statistical significance
Once you complete A/B testing, you’ll be left with a result. Analysis of the statistical significance will help you understand whether the difference between the performance of the two variants is significant enough for you to make changes.
The significance level of a test is 85%, for instance. This means that there’s only a 15% chance that the difference between the two variants is statistically insignificant. The higher the significance level, the more beneficial the implementation of the test result will be. Measuring statistical significance eliminates any shred of doubt and aids in decision-making post A/B testing.
Don’t give up on results that seem insignificant
Even if your test results seem statistically insignificant, don’t quit split testing. Your experiment might fail occasionally, but that mustn’t deter you. A failed test simply signifies that what you’re testing (the element) doesn’t have a major impact on your business currently. The trick is to identify these elements as soon as possible and move on to the next test. However, you can always retest these elements to verify their relevance at a different stage or point in time.
Do define metrics to measure success
Identify metrics that help assess the success of a split test. It could be newsletter subscriptions, event registrations or CTA clicks. Zero in on factors that are measurable. Brand perception, for instance, might be your eventual marketing goal, but it cannot be conclusively measured using a test.
Once you’ve completed an A/B test, it’s time for action. If the result is statistically significant, implement it in your strategy. Otherwise, set the variable aside for a while and move to the next element.