A/B Testing is the process of dividing traffic and comparing two variations of the same element to see which yields the best results.
A/B testing is often used in marketing to determine which marketing message, offer, design or other element is most effective at improving response rates. On the Web, A/B testing is used for website optimization to determine which variations of a page element improve conversion rates the most.
Marketers use A/B testing on their websites to improve conversion rates. For example you may want to determine which call to action on your landing page results in more clicks to the next page. To determine this, you can set up an A/B test with two different variations of the button.
What can be A/B tested?
Almost anything on your website that affects visitor behavior can be A/B tested:
- SaaS Product development: Determine how users interact with your product so you can improve usability and conversion.
- Landing Pages: Use it to understand what elements attract visitors’ attention and entice them to take action.
- Shopping Cart/Checkout: It can be used on this page to make the checkout process easier and more pleasant for visitors, resulting in a higher conversion rate.
- Product Detail Pages: A good layout for this page can increase the chances of adding an item to the shopping cart.
- Category Pages: Your goal for this page is a high degree of usability and to ensure that visitors can easily see and navigate through your products.
- Website copy and structure: You can test the content of your website in the form of headlines, paragraphs, testimonials, Calls to Action (text and buttons) or social proof elements.
If your A/B test was unsuccessful, recordings will show you how people reacted to your proposed change and what went wrong. With this knowledge you’ll be able to introduce a new, better test.
Are there any downsides to A/B testing?
The harsh reality of A/B testing is that most tests fail to either improve the original variant or lose outright. It can be very hard to find a winning test. This reality makes running repeated tests challenging. While failed tests are part of any A/B testing process, there are a few ways to improve your odds for successful tests.
You can look at your analytics data and combine it with qualitative behavioral user insights from Userpeek to find the real issues that are getting in the way of conversions.
By making every A/B test a learning experience (especially the failed ones), you can improve your odds with subsequent tests that don’t repeat the similar mistakes or flawed assumptions. Remember that a solid A/B test hypothesis is an informed solution to a real problem – not an arbitrary guess.
How do I run an A/B test?
Launching an A/B test is a rather easy process. Essentially, it is just six steps:
- Define a hypothesis: Create a hypothesis what should be A/B tested. CTAs, copy, the visual design – it is up to you to decide what you want to test. However, remember to test only one change at a time to make your test reliable. If you want to test different versions of a CTA, change only one thing, be it the color, the copy or the size. Do not mix many changes at once.
- Create variations: Load your website in one of the WYSIWYG editors and create any changes using the simple point-and-click interface. Advanced users can make CSS and JS code changes manually. Note: some of WYSIWYG editors could require you to paste the Session Replay script in every separate variant page.
- Choose your conversion goals: All A/B tests have goals whose conversion rate you want to increase. These goals can be straight forward (clicks on links, visits page, etc.) or could use advanced custom conversion criteria.
- Start and track your test: Reporting is in real-time, so you can start seeing reports as soon as visitors arrive on a live test. At this point, visitors will be randomly assigned to either the control version or to the variant.
- Analyzing the results: Once your experiment is complete, it's time to analyze the results. Your A/B testing software will present the data from the experiment and show you the difference between how the two versions of your page performed, and whether there is a statistically significant difference.
Whatever your experiment's outcome, watch the recordings.
If you have a winning variant, decide which changes made the test successful. The problem with A/B testing is that the positive change might be attributed to something else than you thought. That is why you need to consult the recordings, so your future tests will be more accurate.
What should I do next?
Thanks for learning about A/B testing. If you want to begin the testing, head over to Userpeek sign-up page and create your free trial account to prepare Session Replay.