What Is A/B Testing?

Written by Coursera Staff • Updated on

A/B testing, sometimes called split testing, is a marketing strategy that can improve campaigns and, in turn, drive customer engagement and sales. Explore its uses and benefits for a better understanding of the practice.

[Featured Image] A marketer sits at their laptop at their desk and goes over the results of AB testing conducted by their team.

Key takeaways

A/B testing is a methodology that can help you gather information to make informed decisions, ultimately leading to an enhanced customer experience.

  • To make the most of your A/B tests, set clear goals, test one variable at a time, run tests long enough for reliable data, and seek colleague or customer input.

  • You can use A/B testing to measure cause and effect, understand what customers value, and optimize website, social media, and email components.

  • You can run A/B tests to identify what works, increase engagement, encourage conversations, reduce risk with informed decisions, and refine content to deliver clear, compelling experiences for your audience.

Discover more about who uses A/B testing and why, along with the potential benefits and drawbacks of this type of testing. To learn more about analyzing data using marketing analytics methods, enroll in the Meta Marketing Analytics Professional Certificate program, where you’ll have the opportunity to collect, sort, evaluate, and visualize marketing data; design experiments and test hypotheses; and use Meta Ads Manager to run tests, learn what works, and optimize ad performance. 

What is A/B testing?

A/B testing compares two versions of an application, email, website, or digital element like a headline, to see which is more successful. It's often used in digital marketing, where it can be a helpful way to determine customer preferences. A/B testing a marketing e-mail would involve making two different versions of one email and sending version A to one group and version B to another. You can see which version is more effective by viewing user behavior metrics, like the number of people who clicked links within the email or made a purchase. At the root of A/B testing, you glean helpful information to make informed decisions and optimize the customer experience.

Who uses A/B testing tools?

The results of A/B testing, sometimes called split testing, provide valuable data about what is or isn’t working with the test subject. A/B testing can be used in various experiments across different industries, including tech companies, startups, and marketing. 

If a company is developing software, it can use split testing to enhance the UX, or user experience. It might compare the location of a CTA or call to action, for example, to see if its placement impacts the number of times it's clicked. Marketers aim to capture customers' attention, which can be challenging. Marketers run tests on their websites, emails, and content, looking to make minor adjustments that could result in increased revenue.

When should you use A/B testing?

You may consider using A/B testing to isolate a performance problem when you have, for example, a digital marketing campaign or some component of your strategy that isn’t meeting expectations. A/B testing can also be effective in helping you compare two different approaches for launching a new web page, email campaign, or production release, among other things. 

What is an example of A/B testing?

With A/B testing, it’s important to make the changes between your A version and B version limited to one aspect of your project. If you test multiple changes at once, you won’t know which contributes to your results.

If you want to test an email campaign, you’d change one element, like the header image or subject line. Typical components to test include:

Website

  • CTAs: Size, color, font, shape

  • Headings: Size, font, color, placement

  • Images: Varying pictures, colors, realistic versus animated, placement

  • Product descriptions: Varied lengths, formats

  • Forms: The number of questions asked, including a progress bar, formatting

Social media

  • Use of video or picture

  • Hashtags

  • Post length

  • Use of coupon code

  • Posting at a time of day or day of week

Email

  • Personalized text

  • Email send times

  • Email subject lines

  • Copy length

Statistical interpretation & probability

In statistics, the probability that results are due to random chance is known as p-value. A confidence level of 95%, meaning you are 95% confident that results are accurate, is a standard measurement of success. To ensure a low p-value and high confidence value, it’s important to use a large enough sample size. This will help you avoid measuring a false positive result. Experiments with a high probability of missing differences between variants are known as underpowered tests. Running a test for too short a duration of time or with too few users can lead to underpowered tests.

You can calculate the sample size needed by determining your baseline conversion rate, minimum detectable effect, significance level, and statistical power. Many A/B testing platforms and software today will calculate this for you.

A/B testing tools

Many A/B testing tools exist today. You may be able to find tools that are integrated into a content management system (CMS), but you have plenty of options. Some of the most popular tools available today include:

  • A/B Tasty

  • Optimizely

  • VWO

  • Heap

  • Dynamic Yield

Pros and cons of A/B testing

Using A/B testing allows you to know exactly what does (and doesn’t) work for an improved return on investment (ROI) and enhanced engagement. As you consider A/B testing, weigh the pros and cons of the process, which include:

Pros

1. Quick results: You can set up an A/B test reasonably quickly and get results in as little as two weeks. These short-order tests can guide marketers, website designers, or product developers to ensure their efforts are adequate for their customer base. 

2. Improved metrics: Engagement rates and conversion rates can increase with A/B testing. As you test components, like the size of a call-to-action button, you see which one customers respond to. If you make your winning results live across your site or campaigns, you'll likely see more customers click on it, which drives engagement and, in turn, drives conversions. 

3. Reduced risk: By using A/B testing, you can make informed decisions. Rather than building an entire website and learning about issues upon completion, you can identify improvements as you go and reduce the risk of large-scale, time-intensive changes.

Cons

1. Specific goals yield limited-scope results: While A/B test results might be helpful, they’ll only provide direction on the element tested, which may be small compared to the entire project. 

2. Short-term results: While you can glean valuable information from A/B testing, the sentiment from your audience could change over months or years. A/B testing should be a continual, consistent, ongoing process.

3. Requires time and effort: A/B testing can provide data-based guidance, but it takes time to set up, execute, and track each test.

Best practices in A/B testing

As you consider what to test, follow these suggestions:

  • Define a goal: Before you design your test, consider what you're trying to achieve. If you're testing email marketing, your goal might be to boost click-through rates. With this goal in mind, you'll test only items you believe might influence someone to click the call-to-action button. 

  • Test one item at a time: By testing one change at a time, you can be sure the improved results stem from the specific change you’ve made. Attempting to test more than one thing at a time will leave you wondering which change contributed to its success. 

  • Give your tests time: Looking at results before you reach statistical significance is known as “peeking”. If you’re constantly checking the results for fluctuations, you may believe that you’re noticing a trend when there is no statistical significance. If you stop a test early, you run the risk of receiving no actionable results.

  • Review data with context: Novelty effects refer to consumer enagement, traffic, and conversions that are caused by the excitement, or novelty, of change. After time, the change is no longer “new”, and user behavior reverts to previous levels, even though you’ve kept the winning results of the A/B test live. 

  • Ask others for input: To expand your testing possibilities, ask your colleagues what they think you should test or collect customer feedback that can help guide your tests.

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