In the post below you will learn more about A/B tests, as well as the differences between them and multivariate research.
Table of contents
- 01 What is an A/B test?
- 02 Which elements can you check in A/B tests?
- 03 How is an A/B test created and how do you construct a hypothesis?
- 04 What does the classic approach to A/B testing involve?
- 05 When should you use split URL tests?
- 06 Is an A/B test limited to just two variants?
- 07 What are multivariate tests?
- 08 How to succeed in A/B testing?
- 09 Summary
01 What is an A/B test?
A/B tests are a method used to compare two versions against each other in order to determine which one achieves better results. It is one of the most frequently used methods for maximizing the performance of websites, applications, SaaS products, emails and other elements.
In the classic A/B method, it is you who decides what you would like to analyze and what your goal will be. Next, you create one or more variants of the original element of the website. The following step is to split the traffic on the site between two groups of randomly assigned users according to a defined rule. The final stage is collecting data on the effectiveness of the performance activities on the site. The last of these is summarizing all the data and selecting the variant that performed best while removing the other one.
If the tests are not carried out correctly, they may fail to deliver valuable results, and can even mislead you. Generally speaking, running controlled tests can help your company in:
- solving UX-related problems and improving the areas where the company does not meet customers' needs or expectations,
- improving the effectiveness of site traffic, including: conversion and revenue while lowering the cost of acquiring leads,
- increasing overall engagement (by reducing the bounce rate, as well as improving the click-through rate, etc.).
You must, however, remember that when choosing the right variant you should generalize the collected results in relation to the overall number of potential users. This is a significant point that must be carried out. Otherwise, you are doomed to make a wrong decision that will negatively affect your activities on the site in the long run. The process of obtaining these results is called hypothesis testing, while the data you are looking for is called statistical significance.
02 Which elements can you check in A/B tests?
This type of research allows you to check many elements on a site, among others:
- different ways of arranging the navigation menu,
- optimizing landing pages based on personalization,
- promotional communication and newsletter sign-up banners.
03 How is an A/B test created and how do you construct a hypothesis?
If you want to start A/B testing, you must first identify the problem you want to solve or pinpoint the customer behaviors you would like to reinforce or change. Once you manage to recognize them, this is the point where you should formulate a hypothesis - that is, your assumption, which the research result may confirm or, on the contrary, rule out or contradict.
Example hypothesis: Applying a social proof message to the product page will inform the user about the popularity of the product and, as a consequence, increase the number of times that product is added to the cart by as much as 10%.
In this case, after noticing the problem (a low add-to-cart rate) and developing a hypothesis (that is, adding social proof information to encourage adding the product to the cart), you are ready to run this kind of experiment on your site.
04 What does the classic approach to A/B testing involve?
In a simple A/B test, traffic is split between two content variants. One of them is defined as the control and contains the existing content and appearance. The other, in turn, functions as the new version of the controlled test.
05 When should you use split URL tests?
Split URL tests let you run experiments based on separate URL addresses for each possible version. Launch tests with separate URLs when you already have two existing pages and want to check which one delivers better results.
06 Is an A/B test limited to just two variants?
If you want to run an experiment on more than two options, you can use an A/B/n test. This type lets you measure the performance of three or more variants instead of testing only one against the control group.
07 What are multivariate tests?
This type of research allows you to check changes in a larger number of sections on a single page. Among other things, you can launch a test on one of your landing pages and change, for example, two elements in it. In the first version, add a contact form instead of the main banner. In the second, add a video format. The system will generate other possible combinations based on your changes, which include both video and a contact form. As a consequence, you will get as many as 4 versions:
V1 - Control variant (without a contact form and without a video element)
V2 - Version with a contact form
V3 - Version with a video format
V4 - Version with a contact form and a video format
Multivariate tests generate all possible combinations of your changes; it is not recommended to create a large number of variants unless you are running the experiment on a high-traffic site. On the other hand, running multivariate tests on a site with a low traffic rate can lead to poor results and insufficient data to draw meaningful conclusions. That is why, at the start of your approach to multivariate analysis, make sure your site has at least a few thousand visitors per month.
08 How to succeed in A/B testing?
When running an A/B test, applying the correct methodology is crucial to be able to obtain valuable results even long after it has ended. Try to understand whether the changes you are checking directly influence user behavior, or whether they happen by chance.
09 Summary
A/B tests are one of the most effective methods of optimizing websites, applications and communication - they let you make decisions based on data rather than gut feeling. The key is a well-formulated hypothesis, correct methodology and patience in analyzing the results. When two variants are not enough, reach for A/B/n or multivariate tests, and when you have two ready pages - for split URL tests. Well-run experiments translate into higher conversion and real sales growth in e-commerce.