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What is A/B and Multivariate Testing?

What is A/B testing?

A/B testing—which is also called split testing or bucket testing—is a marketing research methodology for comparing two variations, or versions, of content to evaluate performance. It tests a control version "A" against an alternate version "B" to measure which one is most successful based on your metric. As a digital marketer doing either B2B marketing or B2C marketing, you have different things on which you can use A/B testing. You can test:

  • Web page content by splitting traffic between version A or B while you monitor visitor actions to identify the website version that yields 1) either the highest conversion rate or 2) where on the page visitors perform the desired action
  • Email marketing approaches, such as the subject line, by splitting an audience into A and B segments to determine which version generates a higher open rate
  • Recommendation engine versions to see which one results in more sales

Regardless of what’s being tested, A/B testing helps you determine how to provide the best customer experience (CX).

What does A/B testing stand for?

An A/B test is an experiment that compares a test group against a control group. The "A" and "B" in A/B testing stands for two versions of an experiment: version A (control) or version B (test).

What is an A/B/N test?

In addition to version A (control) and version B (test), the "N" in A/B/N testing stands for "unknown". In other words, an A/B/N test is a type of website test with more than two variations.

When do you do A/B testing?

When do you do A/B testing?

A/B testing can be used to evaluate just about any digital marketing material, including emails, newsletters, ads, text messages, website pages, and mobile apps. You can investigate them deeper and measure the effectiveness of different content images, copy, CTAs, and approaches to provide the best possible customer experience (CX).

A/B testing plays a huge role in determining what’s working and what isn’t in your marketing campaigns. It can indicate what your audience is interested in, intrigued by, and responds to. A/B testing can help you see which element of your marketing strategy has a stronger impact than others and which might need improvement and which might need to be dropped altogether.

How do you perform an A/B test?

A/B testing is not difficult. Here are the basic steps:

  • 1. Determine the testing goal
  • 2. Develop a hypothesis
  • 3. Identify test targets
  • 4. Design control (A) and test (B) variants
  • 5. Utilize a QA tool to validate the setup
  • 6. Execute the test
  • 7. Track and evaluate results
  • 8. Apply learnings to improve CX

Whatever the outcome of your test, you will have statistics and empirical evidence to help you refine and enhance your marketing campaigns. Using what you’ve learned from your A/B test, you can improve your digital marketing assets and content marketing strategies to deliver a bigger impact, design a more engaging customer experience, write more compelling copy, create more captivating visuals, and better connect and engage with your audience. As you continuously optimize, your marketing strategies will become more effective, increasing ROI and driving more revenue.

How to interpret A/B test results

A/B testing is a form of marketing research. It’s important to establish goals while planning a test, so you can evaluate the results, determine a winner, and personalize the rest of the campaign deployment to reflect the winning outcome. In many situations, an audience is presegmented with a holdout group that will receive the winning version of a message. In other cases, long-term testing of automated messages can allow marketers to establish statistical significance before finalizing a test.

The test results will indicate the success of one element over another based on what you’ve decided to measure, such as:

  • number of visitors
  • open rates
  • click-through rates
  • signups (for newsletters, etc.)
  • subscriptions

The two elements are monitored until a statistically sufficient measurement is achieved.

Conversion rates can also be measured in terms of revenue. You might consider sales numbers along with the impact of a change on actual sales revenue. Remember that conversion rates can be any measurable action and are not restricted to ecommerce sites and sales. They can include:

  • sales made
  • leads generated
  • newsletter signups
  • clicks on banner ads
  • time spent on the site

What sort of metrics should you be paying attention to when it comes to A/B testing?

The answer to that question depends on your hypothesis and goals. However, you should note metrics that show how engaged your audience is with your marketing content.

If you are testing a web page, look at the number of unique visitors, return visitors, how much time they are spending on the page, as well as the bounce and exit rates. For an email, you will want to see who opens it and clicks through to your call to action (CTA).

What is A/B testing in digital marketing?

A/B testing in digital marketing, both B2C marketing and B2B marketing, can take many different forms. It can be used to test website experiences, email marketing subject lines, or anything else that contributes to creating a great customer experience (CX) and higher engagement levels.

What are some A/B testing examples?

How to avoid common mistakes with A/B testing?

Anything that can be personalized can be A/B tested. A list of digital marketing elements that can be tested include:

  • Navigation links
  • Call to action (CTA)
  • Design/layout
  • Recommendation engine options
  • Copy
  • Offer
  • Headline
  • Email subject line
  • Friendly from address
  • Images
  • Social media buttons (or other buttons)
  • Logos and taglines/slogans

What are the benefits of A/B testing?

A/B testing in marketing provides a great way to quantitatively determine of what tactics work best with your target audience. In some cases, you may validate a hunch, but in other cases, the results could be unexpected.

How to avoid common mistakes with A/B testing?

An A/B test is a great tool, but if there are more than two options you need to test to determine the best experience, you will likely want to do a multivariate test instead of A/B test.

When should you not use an A/B test?

An A/B test is a great tool, but if there are more than two options you need to test to determine the “best experience,” you will likely want to do a multivariate test instead of A/B test.

To A/B or multivariate test? That is the question.

What is the difference between A/B testing and multivariate testing?

The main difference between A/B testing and multivariate testing is the complexity of the testing. If only two variables need to be testing, an A/B test should be used. If there are more than two variables, a multivariate test should be employed. In both test types, the goal is to find the best customer experience based on whatever success metrics you put in place.