A/B testing

In marketing and business intelligence, A/B testing is jargon for a randomized experiment with two variants, A and B, which are the control and treatment in the controlled experiment. It is a form of statistical hypothesis testing with two variants leading to the technical term, Two-sample hypothesis testing, used in the field of statistics. Other terms used for this method include bucket tests and split testing but these terms have a wider applicability to more than two variants. In online settings, such as web design (especially user experience design), the goal is to identify changes to web pages that increase or maximize an outcome of interest (e.g., click-through rate for a banner advertisement). Formally the current web page is associated with the null hypothesis.

As the name implies, two versions (A and B) are compared, which are identical except for one variation that might affect a user's behavior. Version A might be the currently used version (control), while Version B is modified in some respect (treatment). 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 significant gain in sales. Significant improvements can sometimes be seen through testing elements like copy text, layouts, images and colors, but not always. The vastly larger group of statistics broadly referred to as Multivariate testing or multinomial testing is similar to A/B testing, but may test more than two different versions at the same time and/or has more controls, etc. Simple A/B tests are not valid for observational, quasi-experimental or other non-experimental situations, as is common with survey data, offline data, and other, more complex phenomena.

A/B testing has been marketed by some as a change in philosophy and business strategy in certain niches, though the approach is identical to a between-subjects design, which is commonly used in a variety of research traditions. A/B testing as a philosophy of web development brings the field into line with a broader movement toward evidence-based practice.

Common Test Statistics

"Two-sample hypothesis tests" are appropriate for comparing the two samples where the samples are divided by the two control cases in the experiment. Z-tests are appropriate for comparing means under stringent conditions regarding normality and a known standard deviation. Student's t-test are appropriate for comparing means under relaxed conditions when less is assumed. Welch's t test assumes the least and is therefore the most commonly used test in a two-sample hypothesis test where the mean of a metric is to be optimized. While the mean of the variable to be optimized is the most common choice of Estimator others are regularly used.

History

Google data scientists ran their first A/B test at the turn of the millennium to determine the optimum number of results to display on a search engine results page. While this was the origin of the term, very similar methods had been used by marketers long before "A/B test" was coined. Common terms used before the internet era were "split test" and "bucket test".

As with most fields, setting a date for the advent of a new method is difficult because of the continuous evolution of a topic. Where the difference could be defined is when the switch was made from using any assumed information from the populations to a test performed on the samples alone. This work was done in 1908 by William Sealy Gosset when he altered the Z-test to create Student's t-test.

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, "Offer ends soon! Use code B1".

All other elements of the email's copy and layout are identical. The company then monitors which campaign has the higher 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. A more nuanced approach would involve applying statistical testing to determine if the differences in response rates between A1 and B1 were statistically significant (that is, highly likely that the differences are real, repeatable, and not due to random chance).

In the example above, the purpose of the test is to determine which is the more effective way to impel customers into making a purchase. If, however, the aim of the test had been to see which would generate the higher click-rate – that is, the number of people who actually click onto the website after receiving the email — then the results might have been different.

More of the customers receiving the code B1 might 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 had been simply to see which would bring more traffic to the website, then the email containing code B1 might 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.

Segmentation and targeting

A/B tests most commonly apply the same treatment (e.g., user interface element) with equal probability to all users. However, in some circumstances, responses to treatments may be heterogeneous. That is, while a treatment A might have a higher response rate overall, treatment B may have an even higher response rate within a specific segment of the customer base.

For example, the breakdown of the response rates by gender could have been:

	Overall	Men	Women
Total sends	2,000	1,000	1,000
Total responses	80	35	45
Treatment A	50 / 1,000 (5%)	10 / 500 (2%)	40 / 500 (8%)
Treatment B	30 / 1,000 (3%)	25 / 500 (5%)	5 / 500 (1%)

In this case, we can see that while treatment A had a higher response rate overall, treatment B actually had a higher response rate with men.

As a result, the company might select a segmented strategy as a result of the A/B test, sending treatment B to men and treatment A to women going forward. In this example, a segmented strategy would yield an increase in expected response rates from 5% ((40 + 10) / (500+500)) to 6.5% ((40 + 25) / (500+500)), constituting a 30% increase.

It is important to note that if segmented results are expected from the A/B test, the test should be properly designed at the outset to be evenly distributed across key customer attributes, such as gender. That is, the test should both (a) contain a representative sample of men vs. women, and (b) assign men and women randomly to each “treatment” (treatment A vs. treatment B). Failure to do so could lead to experiment bias and inaccurate conclusions to be drawn from the test.

This segmentation and targeting approach can be further generalized to include multiple customer attributes rather than a single customer attribute – for example, customer age AND gender, to identify more nuanced patterns that may exist in the test results.

Acceptance

Many companies use the "designed experiment" approach to making marketing decisions, with the expectation that relevant sample results can improve positive conversion results. 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.

A/B testing tools comparison

	ABtasty	ClickThroo	Content Experiments	SiteSpect	VisualWebsiteOptimizer	maxymiser	PlanOut	sixpack
Cost	29€	$95	Free	Paid	$49	Paid	Open Source (free)	Open Source (free)
Email campaigns	✓	✓	✗	✓	✓	✓	✓	?
Multivariate testing	✓	✗	✗	✓	✓	✓	✓	?
Within-subjects designs	✗	✗	✗	✗	✗	✗	✓	?
Target platforms	Client-side	Client-side	Client-side	Client-side	Client-side	Client-side	Server and client-side	Server and client-side
Interface	Graphical	Graphical	Graphical and API	Graphical	Graphical	Graphical	API	Graphical and API

See also

• Choice modelling
• Google Analytics Content Experiments (formerly Google Website Optimizer)
• Multivariate testing
• Multi-armed bandit
• Student's t-test