A & B Testing

A/B testing takes the guesswork out of website optimization and enables data-informed decisions that shift business conversations from “we think” to “we know.”

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. AB testing is essentially an experiment where two or more variants of a page are shown to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal.

 

A & B Testing

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How it works

A/B testing takes two versions of a web page and shows each of them to half of your traffic. Engagement data from the original and the variant are then collected for analysis.

The Process

In order to obtain the best results from A/B testing, it is important to follow a process that takes you from identifying your goals ahead of time, to analyzing results.

Why use it?

A/B testing allows you to optimize user experience, whether it be on your blog post or on your landing page, which can save you money on marketing campaigns due to efficiency.

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).

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.

A & B Testing

The A/B testing process

The following is an A/B testing framework you and your team can utilize 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. 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.
A & B Testing

Why you should utilize A/B testing

A/B testing allows individuals, teams, and companies to make careful changes to their user experiences while collecting data on the results. This allows them to construct hypotheses, and to learn better why certain elements of their experiences impact user behavior. In another way, they can be proven wrong—their opinion about the best experience for a given goal can be proven wrong through an A/B test.

More than just answering a one-off question or settling a disagreement, AB testing can be used consistently to continually improve a given experience, improving a single goal like conversion rate over time.

For instance, a B2B technology company may want to improve their sales lead quality and volume from campaign landing pages. In order to achieve that goal, the team would try A/B testing changes to the headline, visual imagery, form fields, call to action, and overall layout of the page.

Testing one change at a time helps them pinpoint which changes had an effect on their visitors’ behavior, and which ones did not. Over time, they can combine the effect of multiple winning changes from experiments to demonstrate the measurable improvement of the new experience over the old one.

This method of introducing changes to a user experience also allows the experience to be optimized for a desired outcome, and can make crucial steps in a marketing campaign more effective.

By testing ad copy, marketers can learn which version attracts more clicks. By testing the subsequent landing page, they can learn which layout converts visitors to customers best. The overall spend on a marketing campaign can actually be decreased if the elements of each step work as efficiently as possible to acquire new customers.