# An introduction to A/B testing and optimization

## A comprehensive guide to A/B testing, explaining the differences between A/B and multivariate testing, how to conduct tests in a structured and progressive way, and the thought process behind choosing the right experiment.

**Summarize this article**

*   A/B testing is a scientific method for comparing two versions of a webpage or app to see which performs better for a specific goal, like increasing conversions or user engagement.
*   It’s a powerful tool for optimizing websites, mobile apps, emails, and more, and can help solve UX issues, improve performance, and boost engagement.
*   To run an A/B test, you first define a problem or user behavior you want to address. Then, you create variations of your original element and split website traffic between them. Finally, you collect and analyze data to see which variation performs best.
*   Common A/B tests include testing different navigation menus, optimizing landing pages, and experimenting with promotional messages.

A/B testing is a method of comparing two versions of a webpage or app against each other to determine which one performs better against a specific objective. It is one of the most widely used techniques for maximizing the performance of digital assets such as websites, mobile applications, SaaS products, emails, and more.

Controlled experiments provide marketers, product managers, and engineers with the agility to iterate fast and at scale, leading to data-driven, thoroughly informed decisions about their creative ideas. With A/B tests, you can stop wondering why some things are not working, because the proof is in the pudding. It’s the perfect method to improve conversion rate, increase revenue, grow your subscribers base, and improve your customer acquisition and lead generation results.

## What is an A/B Test?

In a classic A/B testing procedure, we decide what we would like to test and what our objective is. Then, we create one or more variations of our original web element (a.k.a. the control group, or the baseline). Next, we split the website traffic randomly between two variations (i.e., we randomly allocate visitors according to some probability), and finally, we collect data regarding our web page performance (metrics). After some time, we look at the data, pick the variation that performed best, and cancel the one that performed poorly.

If not done correctly, tests can fail to produce meaningful, valuable results and can even mislead. Generally speaking, running controlled experiments can help organizations with:

1.  Solving UX issues and common visitor pain points
2.  Improving performance from existing traffic (higher conversions and revenue, improve customer acquisition costs)
3.  Increasing overall engagement (reducing bounce rate, improving click-through rate, and more.)

We must keep in mind that the moment we pick a variation, we are generalizing the measures we collected up to that point to the entire population of potential visitors. This is a significant leap of faith, and it must be done in a valid way. Otherwise, we are eventually bound to make a bad decision that will harm the web page in the long run. The process of gaining validity is called *hypothesis testing*, and the validity we seek is called *statistical significance*.

**Some examples of A/B tests:**

*   Testing different sorting orders of the site’s navigation menu
*   Testing and optimizing landing pages
*   Testing promotional messages, like newsletter subscription overlays and banners

## How an A/B test is born: Constructing a hypothesis

An A/B test starts by identifying a problem that you wish to resolve, or a user behavior you want to encourage or influence. Once identified, the marketer would typically conclude a hypothesis – an educated guess that will either validate or invalidate the experiment’s results.

**Example hypothesis**: Adding a Social Proof badge to your Product Detail Pages (PDP) will inform visitors of the product’s popularity and increase add-to-cart events by 10%.

In this case, once the problem is identified (low add-to-cart rate, as an example) and a hypothesis is worked out (adding a social proof badge to encourage more website visitors to add items to their carts), you are ready to test it on your site.

## The classic approach to A/B testing

In a simple A/B test, traffic is split between two variations of content. One is considered the control and contains the original content and design. The other functions as a new version of the controlled variation. The variation may be different in many aspects. For example, we could test a variation with different headline text, call-to-action buttons, a new layout or design, and so on.

In a classic page-level experiment, you don’t necessarily need two different URLs to run a proper test. Most A/B testing solutions will let you create variations dynamically by modifying the content, layout, or design of the page.

However, if you have two (or more) sets of pages that you’re looking to include in a controlled test, you should probably consider using a split URL test.

## When to use split URL tests

Split URL testing, sometimes referred to as “multi-page” or “multi-URL” testing, is a similar method to a standard A/B test, which allows you to conduct experiments based on separate URLs of each variation.

With this method, you can conduct tests between two existing URLs, which is especially useful when serving dynamic content. Run a split URL test when you already have two existing pages and want to test which one of them performs better.

For example, if you’re running a campaign and you have two different versions for potential landing pages, you can run a split URL test to examine which one will perform better for that particular campaign.

## An A/B test is not limited to just two variations

If you want to test more than just two variations, you can run an A/B/n test. A/B/n tests allow you to measure the performance of three or more variations instead of testing only one variation against a control page. High-traffic sites can use this testing method to evaluate the performance of a much broader set of changes and maximize test time with faster results.

However, although it is useful for any testing, from minor to dramatic changes, I recommend not making too many changes between the control and variation. Try making just a few critical and prominent changes to understand the possible causal reasons for the results of the experiment. If you are looking to test changes to multiple elements on a web page, consider running a multivariate test.