What Is A/B Testing? Definition & How to Do It?

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A/B test

What is an A/B Test?

The Definition

An A/B test pits two items or variations against one another to see which performs better. A/B testing is frequently used in product management to find the most appropriate functioning option.

An A/B test is essentially an experiment for developing, launching, and comparing two versions of the same product. Organizations may use A/B testing to analyze user feedback on any particular ad, banner, or something else.

Performing an A/B test is a common strategy in today’s market to analyze the customer’s responses to two different products and understand which one could serve better.

A/B testing is among the efficient product management tools that eliminate space for any guesswork in the product. It also enables the developers to make data-backed decisions. 

The two products to be tested may not be so different, i.e., there could be just a subtle difference, an image, font, audio, or something else. 

The test aims to understand whether this subtle difference could make the product more appealing to the user.

Before launching any A/B test, an important detail is to clarify what type of data you intend to focus on. It prevents you from wasting time on tests that would not achieve the desired results. The results must be related to your ongoing operations.

The duration of any A/B test is up to you. But there must be enough time to gather a large user audience to test both products. It results in obtaining a sample size sufficient to draw any conclusions.

Why Is A/B Testing Important?

A/B tests provide the information needed to maximize your marketing spend. Assume your supervisor has provided you with a budget to use Google AdWords to increase visitors to your website. 

You run an A/B test to see how many times three different article titles get clicked. You run the experiment for a week. It ensures you’re running the same number of ads for each option on any given day and time.

The experiment results will assist you in determining which title receives the most clicks. You can then utilize this information to improve your campaign, increasing its return on investment (ROI) over time.

How To Do A/B Testing?

Choose a target:

Every A/B test, whether for an ad, a web page, or an email, needs a clear purpose. What you want to know or how a change could make things better could be the goal. What metrics or conversions are you aiming for?

Make an assumption:

The test will use an A/B hypothesis. Why will one version outperform the other? Ask a question and forecast the answer to the barrier to conversion to aid in formulating the hypothesis.

Create and execute the test:

A and B are the two versions to test. Choose only one variable to alter. Multi-variant testing increases the test’s complexity. Here, beginners should start with the basics, and you can test almost anything. Choose something simple to alter that will have a significant impact.

Examine the outcomes:

Observe the test (Check out the tools link below for options.) to ensure everything is working correctly, but don’t look at the findings until the test is over. It’s tempting to declare a winner too early. 

The test should go long enough to produce meaningful, statistically significant results. It entails reliably having sufficient data to implement changes in response to the findings.

Incorporate the outcomes:

Adopt the big winner, but keep testing. A/B testing is all about incremental variations, and data from subsequent tests will aid this process. You will learn more effectively if you take more tests.

We recommend using top product management software like Chisel, which lets you create roadmaps, has A/B testing survey templates, and many other exciting tools.

Advantages of A/B Test

Increased user engagement:

Many pages, banners, or ad elements can be A/B tested. Testing one element at a time to check which affected the user’s behavior the most will help a business increase user engagement. It ultimately leads to better sales.

Reduced Bounce Rates:

A/B testing reflects the combination of elements that are most likely to keep the user on the app or site longer. The more time they spend, the more probability of discovering how good your product or service is. It results in reducing bounce rates.

Higher conversion rates:

A/B testing is one of the most simple and effective ways to determine the best content. The content aims to transform site visits into signups, purchases, and so on. 

You will have a clear idea about what works and what doesn’t, which helps design more relatable content. It converts more leads that appeal to the user and keep them engaged.

Reduced Risks:

An A/B test helps determine what commitments in a product would render ineffective. No business can afford to make ill-informed decisions in today’s competitive market

Here is where A/B tests come in. It helps avoid time-wasting and futile steps and highlights decisions likely to garner a positive response from the user. 

Once the test is over, you will clearly see what suits your campaign the most, thus significantly reducing the risk component. As a product manager, you can use various A/B testing tools available to perform successful A/B testing, as discussed earlier.

Quick results:

Even an A/B test of relatively small sample size can provide significant value that may result in the most engaging responses from users. It enables short-order optimization of new websites, apps, banners, and ads.

Examples of an A/B Test

Landing Pages

Your landing page plays a considerable part in the user’s first impression of a website. It can even affect how you convert the visits into purchases and signups.

The landing page must appeal to the visitors and grab their attention for any successful website. An A/B test can be helpful to determine which landing page could drive the most conversions to sales and have the least bounce rates.

With a clear “winner,” you could determine what page to use to get maximum user interaction on your page.

Best messaging for ads

In the case of PPC ads, A/B testing helps you to exclusively test the keywords and headlines that could perform better than the others in various ads.

It plays a vital role in determining whether your PPC campaign will be a success or a failure. 

You can keep changing subtle details in your ads to determine what catches the eye of the user the most. It gives you a clear picture of how your advertisement should seem.

Determining the correct prices of the product

Whenever a product gets launched, the most challenging part could be to determine what should be the right price for your product. It is where A/B testing comes into play.

A/B testing is an efficient way to determine what price tag for your product would be adequate to maximize your revenue.

What Are the A/B Testing Mistakes To Avoid?

Split-Testing the Incorrect Page:

Testing the wrong pages is one of the most common issues with A/B testing. Split testing that isn’t necessary is a waste of time, resources, and money.

Invalid Hypothesis:

Not having a sound hypothesis is one of the most critical A/B testing mistakes to avoid.

An A/B testing hypothesis is an assumption that gets tested in two ways. 

An A/B testing hypothesis is a theory that explains why you see specific results on a website and how you can improve them.

Miscalculations in Timing:

Timing is crucial in A/B testing. There are several common timing errors like comparing distinctive periods and not running the tests for sufficient time.

Incorrectly calculating results:

Even though measuring outcomes is just as essential as testing, it’s among the most common areas. It is where people make expensive A/B testing blunders. You can’t trust your data or make data-driven marketing plans if you don’t measure your results effectively.


Can the A/B test be used only for development purposes?

Other uses for A/B testing exist. In 2007 Barack Obama’s presidential campaign used A/B testing to determine how to get the voters’ attention. Also, for the things they wanted.

How to calculate sample size for A/B test?

There are many formulas to calculate the sample size for the A/B test. But at the end of the day, the sample size should be what you feel would conclusively reflect your consumers’ best interests.

How long should you run an A/B test?

Specialists recommend that you run your test for a least one to two weeks to gather a representative sample and accurate data. You’ll have addressed the various days’ visitors engage with your website if you do it this way.

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