A/B Testing

A/B testing  is a method of comparing two versions of a webpage, app, advertisement, etc. against each other to determine which one performs better. AB testing is essentially an experiment where two or more variants of promotional material are shown to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal.

Running an AB test that directly compares a variation against a current experience lets you ask focused questions about changes to your promotional material, and then collect data about the impact of that change.

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

As visitors are served either the control or variation, their engagement with each experience is measured or the conversion rate is measured. You can then determine whether changing the experience had a positive, negative, or no effect on visitor behavior.

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.

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.

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. Effectively, AB testing encourages customers further down the customer relationships funnel. 

Student Activity 

• • Define objectives: What are you trying to achieve with your ad? Once you know what you’re trying to do – drive awareness, trial, sales, etc – you’ll be able to layout your test plan.

  • State your hypotheses: Now that you have goals, what do you THINK will perform best? And more importantly, will you be able to take action based on the results?

  • Create a list of what you want to test: What do you want to test through a survey? Potential things to test include how the ad looks (color, photos, graphics), what the ad says (headlines, ad copy, call to action) and the overall design (ad concept, layout, number of visual elements).

  • Start testing

Sources

• • https://www.optimizely.com/optimization-glossary/ab-testing/#