A/B testing

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A sample A/B testing flowchart diagram

A/B testing, split testing or bucket testing is a method of marketing testing by which a baseline control sample is compared to a variety of single-variable test samples in order to improve response or conversion rates. A classic direct mail tactic, this method has been recently adopted within the interactive space to test tactics such as banner ads, emails, landing pages or even entire websites. For instance, on an e-commerce website the purchase funnel is typically a good candidate for A/B testing, as even marginal improvements in drop-off rates can represent a lot of additional sales.

Significant improvements can be seen through testing elements like copy text, layouts, images and colors. However, not all elements produce the same improvements, and by looking at the results from different tests, it is possible to identify those elements that consistently tend to produce the greatest improvements.

Users of A/B testing will distribute multiple samples of a test, including the control, to see which single variable is most effective in increasing a response rate or other desired outcome. The test, in order to be effective, must reach an audience of a sufficient size that there is a reasonable chance of detecting a meaningful difference between the control and other tactics: see Statistical power.

An emailing campaign example

A company with a customer database of 2000 people decides to create an email campaign with a discount code in order to generate sales through its website. It creates an email and then modifies the Call To Action (the part of the copy which encourages customers to do something — in the case of a sales campaign, make a purchase).

• To 1000 people it sends the email with the Call To Action stating "Offer ends this Saturday! Use code A1",
• and to another 1000 people it sends the email with the Call To Action stating "Limited time offer! Use code B1".

All other elements of the email's copy and layout are identical. The company then monitors which campaign has the highest success rate by analysing the use of the promotional codes. The email using the code A1 has a 5% response rate (50 of the 1000 people emailed used the code to buy a product), and the email using the code B1 has a 3% response rate (30 of the recipients used the code to buy a product). The company therefore determines that in this instance, the first Call To Action is more effective and will use it in future sales.

In the example above, the purpose of the test is to determine which is the most effective way to impel customers into making a sale. If, however, the aim of the test was to see which would generate the highest click-rate - i.e., the number of people who actually click onto the website after receiving the email — then the results may have been different.

More of the customers receiving the code B1 may have accessed the website after receiving the email, but because the Call To Action didn't state the end-date of the promotion, there was less incentive for them to make an immediate purchase. If the purpose of the test was simply to see which would bring more traffic to the website, then the email containing code B1 may have been more successful. An A/B Test should have a defined outcome that is measurable, e.g. number of sales made, click-rate conversion, number of people signing up/registering etc^[1].

This method differs from multivariate testing, which applies statistical modeling by which a tester can try multiple variables within the samples distributed.

Companies well-known for using A/B testing

Many companies use the "designed experiment" approach to making marketing decisions. It is an increasingly common practice as the tools and expertise grows in this area. There are many A/B testing case studies which show that the practice of testing is increasingly becoming popular with small and medium-sized businesses as well. ^[2]

While it is widely used behind the scenes to maximize profits, the practice occasionally makes it into the spotlight:

• Amazon.com pioneered its use within the web e-commerce space.^[3]
• BBC^[4]
• eBay^{[citation needed]}
• Biglight^[5]
• Google^[6]
• Microsoft^[7]
• Netflix^[8]
• Playdom (Disney Interactive)^{[citation needed]}
• Zynga^[9]

A/B Testing Tools

Many A/B testing tools are actively developed. Some are available under an open source license or free:

• Google Website Optimizer (server-side tagging required)

Other solutions are commercially supported, generally offering a broader range of features:

• Omniture Test&Target
• Personyze

Other terms used

• A/B/N Testing: A/B testing with more than two options ("N" cells).
• A/B/..Z Testing: Same as above.
• A/B/A: Only two alternatives, but one is repeated. This enables a quick visual of when the test reaches significance.
• Multivariate Testing: A designed experiment where effects from two or more potential causal factors can be isolated from one another.
• Multivariant Testing: same as above.

See also

• Choice modelling
• Adaptive control
• Multivariate testing

References