A/B testing is the process of comparing two or more versions of marketing messages at the same time to see which one performs best. It’s the most popular data-based approach to conversion rate optimization.
In this post, we’ll discuss why you should perform A/B tests, what to test, the different types of A/B tests, and how to evaluate the results.
Let’s jump in!
Why should you run A/B tests?
Creating a high-converting message is more of a science, than an art form—and this is also true for popups. Even when you know your customers and understand their needs, it can still be challenging to create sub-optimal messages for them on your first try. In many cases, you’ll have several different ideas (often called hypotheses) in terms of the value proposition, message, design, targeting, etc.
While you might have the gut feeling that some versions might convert better than others, in most cases these hypotheses turn out to be false. But luckily, A/B testing can give you the answers to your questions.
What should you A/B test?
There are tons of things you can A/B test when creating a marketing campaign. Below are the most important configurations that are worth testing:
1. Messages: What should be your value proposition? Which incentive should you use? Which headline would resonate better with your customers? What color should you choose? What should be your call-to-action?
2. Segments: Should you show this popup to all visitors or only to cart abandoners? Does it work for each traffic source? Should you follow up with these users after they sign up for their discount codes? Is your offer worth it for visitors to subscribe, or should you give them an incentive without requiring a subscription?
3. Overall performance: How much revenue will this campaign generate? What is your ROI from this tool? How much extra money can you make using popups?
Testing all these requires different approaches to A/B testing and we’ll cover that next.
Types of A/B tests
There are two main types of A/B testing:
1. Campaign-level A/B tests: Testing two or more variants of one campaign against each other is called Campaign-level A/B testing.
It works by splitting those visitors into two segments, which are supposed to see the campaign (meaning they are about to see the popup). Then you’ll display one variant of the campaign to one subgroup and the other variant to the other subgroup.
Campaign-level A/B testing is most applicable for testing the effectiveness of different messages, headlines, calls-to-action, design styles, etc.
2. Store-level A/B tests: Splitting the visitors into two or more subgroups from the moment they arrive at your store (regardless of being targeted with a popup or not) will allow you to test not only messages but high-level concepts. So for example, it’ll tell you if it’s worth using popups on your website or not.
You can test different segments, message combinations, and the overall performance of your popup campaigns.
Now let’s see how you can evaluate the results of your A/B tests.
Evaluating the results
Evaluating the results of your experiments depends on what you want to measure.
In general, you have 3 main data sources you can use:
1. Campaign statistics: This is the easiest to check because it’s available on the OptiMonk admin panel when you go to check or edit a campaign. It’s suitable for getting your popup-related metrics like impressions, conversions, and conversion rates.
2. Built-in analytics: Our built-in analytics is the most reliable source to measure assisted revenue. It can connect all 1st-party data sources (not just anonymous information like Google Analytics). It can measure In-session attribution, longer-term attribution, and 5-day attribution. It’s currently available to subscribers upon request.
3. Google Analytics: Google Analytics is the de-facto standard for website analytics, but you can also use it to effectively measure the performance of your popups.
Here you can find a detailed guide on how to utilize each data source.
A/B testing is one of your most powerful allies in data-driven marketing and conversion rate optimization. If you learn how to use it, this method will generate more successful marketing campaigns. It will help you avoid costly mistakes and can increase your conversion rates in the long term.