A/B Testing

What Is A/B Testing?

A/B testing (also known as split testing or bucket testing) is a method of comparing two versions of a webpage or app against each other to determine which one performs better. By creating an A and B variant and testing them against each other, you can use data & statistics to validate new design changes and improve your conversion rates.

AB testing with Optimizely

Running an AB test that directly compares a variation against a current experience lets you ask focused questions about changes to your website or app, and then collect data about the impact of that change. Testing takes the guesswork out of website optimization and enables data-informed decisions that shift business conversations from "we think" to "we know." By measuring the impact that changes have on your metrics, you can ensure that every change produces positive results.

How A/B Testing Works

In an A/B test, you take a webpage or app screen and modify it to create a second version of the same page. This change can be as simple as a single headline or button, or be a complete redesign of the page. Then, half of your traffic is shown the original version of the page (known as the control) and half are shown the modified version of the page (the variation).

A/B testing process

As visitors are served either the control or variation, their engagement with each experience is measured and collected in an analytics dashboard and analyzed through a statistical engine. You can then determine whether changing the experience had a positive, negative, or no effect on visitor behavior.

Why You Should A/B Test

A/B testing allows you to make data-driven decisions instead of hunches and guesses. Rather than launching features to your website or app and then hoping for the best, testing allows you to confirm or disprove your hypotheses before committing to changes.

Testing allows you to optimize your site or app experience to improve conversion rates in a methodical way. A higher conversion rate means getting more value from your existing users instead of having to pay more on traffic acquisition.

Aside from business results, testing can help to transform your workplace culture and create a more data-driven environment. Rather than simply following the opinion of the Highest Paid Person's Opinion (aka HIPPO) you can use data and facts to determine the direction of your product.

Whether you are a marketer, designer, or developer, AB testing is a simple way to use the power of data & statistics to reduce risks, improve results, and become more data-driven in your work.

A/B Testing Process

The following is an A/B testing framework you can use to start running tests:

  • Collect Data: Your analytics will often provide insight into where you can begin optimizing. It helps to begin with high traffic areas of your site or app, as that will allow you to gather data faster. Look for pages with low conversion rates or high drop-off rates that can be improved.
  • Identify Goals: Your conversion goals are the metrics that you are using to determine whether or not the variation is more successful than the original version. Goals can be anything from clicking a button or link to product purchases and e-mail signups.
  • Generate Hypothesis: Once you've identified a goal you can begin generating A/B testing ideas and hypotheses for why you think they will be better than the current version. Once you have a list of ideas, prioritize them in terms of expected impact and difficulty of implementation.
  • Create Variations: Using your A/B testing software (like Optimizely), make the desired changes to an element of your website or mobile app experience. This might be changing the color of a button, swapping the order of elements on the page, hiding navigation elements, or something entirely custom. Many leading A/B testing tools have a visual editor that will make these changes easy. Make sure to QA your experiment to make sure it works as expected.
  • Run Experiment: Kick off your experiment and wait for visitors to participate! At this point, visitors to your site or app will be randomly assigned to either the control or variation of your experience. Their interaction with each experience is measured, counted, and compared to determine how each performs.
  • Analyze 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.

If your variation is a winner, congratulations! See if you can apply learnings from the experiment on other pages of your site and continue iterating on the experiment to improve your results. If your experiment generates a negative result or no result, don't fret. Use the experiment as a learning experience and generate new hypothesis that you can test.

Whatever your experiment's outcome, use your experience to inform future tests and continually iterate on optimizing your app or site's experience.

A/B Testing Ideas

The following is a list of ideas to get you started with testing. A/B testing best practices for what to test can vary by industry, so the ideas are broken up by vertical:


A media company might want to increase readership, increase the amount of time readers spend on their site, and amplify their articles with social sharing. To achieve these goals, they might test variations on:

  • Email sign-up modals
  • Recommended content
  • Social sharing buttons

A travel company may want to increase the number of successful bookings are completed on their website or mobile app, or may want to increase revenue from ancillary purchases. To improve these metrics, they may test variations of:

  • Homepage search modals
  • Search results page
  • Ancillary product presentation

An e-commerce company might want to increase the number of completed checkouts, the average order value, or increase holiday sales. To accomplish this, they may A/B test:

  • Homepage promotions
  • Navigation elements
  • Checkout funnel components

A B2B company might want to increase the number of high-quality leads for their sales team, increase the number of free trial users, or attract a specific type of buyer. They might test:

  • Lead form components
  • Free trial signup flow
  • Homepage messaging and call-to-action

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