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What is A/B testing?

Everything you need to know about A/B Testing, all in one place
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Introduction

We live in an era of data-driven marketing, far away from the times when marketers simply made decisions based on guesswork and intuition and hoped for favorable outcomes. The modern day marketer has a scientific approach and relies on data. And A/B testing is the best way to remove uncertainty and gut feel when making marketing or design decisions for websites, ads or other digital campaigns.

Embrace the experimentation mindset.

By basing your strategy on data and A/B tests, you’ll be agile but most importantly you’ll have guaranteed feedback on what works and what doesn’t. You’ll be better placed to make sound business decisions and to invest time and money in what your visitors actually want.

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A/B testing to drive marketing success

75% of websites with more than 1 million monthly visitors already run A/B testing programs.

But successful A/B testing requires preparation and education, and time and effort to put into practice. You’ll have to create a process, put a framework in place, learn about statistics, set up and learn a new tool and make sure you’re actually getting accurate results. But the effort and time is worth it, given the potential to achieve your marketing goals.

To help we’ve brought together in-depth content on A/B testing, from the best blogs and experts.

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What is A/B testing (or Experimentation/Split testing)?

Definition: A/B testing is an online experiment conducted on a website, mobile application or ad, to test potential improvements in comparison to a control (or original) version. Put simply, it allows you to see which variation (version) works better for your audience based on statistical analysis.

 

A/B testing is also known as split testing, which can be either exactly the same thing as A/B testing or mean split URL testing. For a classic A/B test, the two variations are on the same URL. Alternatively with split URL testing your changed variation is on a different URL (although this is hidden from your visitors).

What about multivariate testing (MVT)?

Sometimes, you want to test several changes on a page, for example the banner, header, description and video. To test all of these elements at the same time, you use multivariate testing (or MVT).

In this case you have multiple variants generated to test all the different combinations of these changes to determine the best one.

The big downside of multivariate testing is that it requires an enormous amount of traffic to be statistically accurate. Before starting a multivariate testing project, you need to check that your audience is high enough to provide representative results.

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Dynamic traffic allocation or multi-armed bandit testing

Multi-armed bandit testing (or dynamic traffic allocation) is when your algorithm automatically and gradually redirects your audience toward the winning variation.

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A/B/n testing

A/B/n testing is when you test more than two variations of an element or page. For example, you could test six versions of a page and do an A/B/C/D/E/F test.

 

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What are the benefits of A/B testing?

Why should YOU do A/B testing? Put simply, it will help maximize the investment you’ve made in bringing traffic to your site. Once you have traffic, increasing your conversions is much less expensive than going out again and finding more. Optimizing the experience for your visitors therefore has a high potential ROI. But A/B testing also delivers other benefits:

  • Learn deeply about your audience with every test: what they like, how they react, their needs and habits.
  • Remove gut decisions from your marketing strategy by adopting an experimentation culture and testing everything.
  • Focus your time and money on what your visitors respond to best, thanks to the results of your A/B tests.

 
With A/B testing you’ll be able to confidently answer these key questions:

  • Which elements drive sales, conversions or impact user behavior?
  • Which steps of your conversion funnels are underperforming?
  • Should you implement this new feature or not?
  • Should you have long or short forms?
  • Which title for your article generates more shares?

How does A/B testing work?

You compare the current version (control) of a page/element against one (or more) variations of it with the changes you want to test. This could be a website page, an element in a page, a CTA, a picture, or bigger changes to the customer journey.

You divide your traffic into equal portions, and visitors are then randomly exposed to one or the other variation during the set period of time when the test is running. Then, their relative performance (in terms of metrics such as conversions or sales) are compared and analyzed to determine if the change(s) are worth implementing.

GOING DEEPER INTO THE INNER WORKINGS OF A/B TESTING

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Why Conversion Rate Optimization (CRO) is key to your A/B testing success

What’s Conversion Rate Optimization (CRO)?

A conversion is when a visitor either buys something or converts in some other way, such as giving you their contact details or booking an appointment. In other words, it is about moving your visitors through the customer buying journey.

How to run A/B tests: frameworks and methodology

The success of great A/B testing is all down to the process. It’s a scientific experiment, so your process has to be rigorous, with strong prioritization to focus on the most valuable tests.
Each company has its unique process but it usually resembles something like this

1

MEASURE, STUDY, ANALYZE YOUR WEBSITE DATA. IDENTIFY THE PROBLEMS AND OPPORTUNITIES

3

PRIORITIZE YOUR TEST IDEAS: ONE OF THE MOST USED PRIORITIZATION FRAMEWORKS IS PIE, FIRST CREATED BY WIDERFUNNEL

With this framework, you rank your test ideas by looking at three criteria to determine which you should run first:

  • Potential ./10: How much room for improvement is there on this (these) page(s)?
  • Impact ./10: How valuable is the traffic on this (these) page(s)?
  • Ease (of implementation) ./10: How easy will this test be to implement on your site?

You then average the three and you’ll know which tests to do first. There are multiple other frameworks available so try them and work out which best suits your needs.

4

Test your highest priority hypothesis

5

Analyze your test results and learn from them

6

COMMUNICATE RESULTS TO STAKEHOLDERs

7

Repeat

What to A/B test: ideas for your experiments

You can basically test everything on your website:
 

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But sometimes, you need inspiration. So here are examples of multiple A/B testing ideas to look through.

Note : Do bear in mind that what worked for others might not work for you. Don’t blindly apply other people’s ideas - make sure you thoroughly analyze to see if it’s relevant for you and how/if you can adapt it for your business.

Dozens of A/B tests ideas for you to get inspired

A/B testing best practices and common mistakes

A/B testing can be hard, and there are plenty of ways it can go wrong. So it’s good to be aware of potential problems and to put safeguards in place. 

Make sure you are set up for success by studying best practices and possible issues that can arise. But as with test ideas, don’t take everything at face value. Put things to the test and see if it applies to your circumstances.

A/B testing reporting & results

A/B testing is about making data-driven decisions AND learning. This means that your reporting and results are vital to your program, whether it is  about educating yourself, communicating to colleagues or getting ideas for your next tests.

How to handle A/B testing results and reporting

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A/B testing statistics and how to understand them

A/B testing is based on statistical methods. While you don’t need to know all the math involved in analyzing your results, having a basic knowledge of statistics will improve your chances of success.

There are two main statistical methods used by A/B testing solutions. One isn’t better than the other, they simply have different uses.

Frequentist approach

This allows you to see the reliability of your results thanks to a confidence level: if this is at a level of 95% or more, you have a 95% chance of it being accurate. But this method has a downside: it has a ‘fixed horizon’, meaning that the confidence level is valueless up until the end of the test.

Bayesian approach

This provides a result probability as soon as the test starts, so there is no need to wait until the end of the test to spot a trend and interpret the data. But this method also has challenges: you need to know how to read the estimated confidence interval given during the test. With every additional conversion, the trust in the probability of a reliable winning variant improves.

A/B testing skills & management processes

To tip the odds in your favor, there are a number of skills and management  processes you can focus on, such as web analytics, UX design and communicating results.

Sharpen your skills for better A/B tests

 
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A/B testing tools to maximize your chances of success

Be it project management, sample size or duration calculator, or toolkits for your processes, there are many resources out there to help you succeed.

Supplement your A/B testing software with these tools

Useful A/B Testing blogs to read

There are some great blogs available, containing useful A/B testing, experimentation and Conversion Rate Optimization-related content that will help you to learn, get inspired, and run better A/B testing programs.

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Partner with these A/B testing agencies to outsource your programs

Outsourcing your A/B testing can be a great way to run programs if you don’t currently have the necessary resources, helping you learn at the same time. Here are some of the best ones out there.
Note: we sell an A/B testing tool but we can also support you with your testing programs, learn more on our customer success page.

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