Top 20+ what is a b testing

Here are the best information about what is a b testing voted by users and compiled by us, invite you to learn together

Video What is a b testing

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

But successful A/B testing requires preparation, education, 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 getting accurate results. But the effort and time are 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.

Further Reading:

A/B testing: The criteria to look for when choosing a solution
Training course: A/B testing & experimentation
What you need to know about A/B testing before you start

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.

Table of Contents

1 Types of A/B testing

What is split testing?

A/B testing is also known as split testing, which can be either 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 simultaneously, you use multivariate testing (or MVT). In this case, you have multiple variants generated to try 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.

What is an A/A test?

A/A tests enable you to test two identical versions of an element. The traffic to your website is divided into two, with each group exposed to the same variation. Through this, you will determine whether the conversion rates in each group are similar and confirm that your solution is working correctly.

Introductory resources on A/B testing to help you get started

A/B Testing White Paper: Best practices, tips and tricks for a successful A/B testing project
Email course: Introduction to A/B Testing certification

2 What are the benefits of A/B testing?

A/B testing enables you to check your hypotheses by directly testing your idea on a target segment of your audience. This way, any modification made to your website will be based on solid facts. A/B testing has many advantages:

Increase conversions

Optimize your website continuously to improve the visitor experience and general conversion rate.

Engage visitors around your brand

Give your visitors an exceptional user experience to engage them around your brand and retain them over the long term.

Get to know your visitors better

Analyze how the different elements of your pages impact your visitors’ behavior and learn more about their needs and expectations.

Make decisions based on quantified results

A/B test your hypotheses and reduce all risk factors. Make decisions based on reliable facts and statistics rather than subjective assessment.

Optimize your time and budget

Channel your efforts (and your budget) into what works best for all of your audience, using what you learn from your A/B tests.

With A/B testing you’ll be able to confidently answer these 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?

3 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.

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.

Further Reading:

A/B testing: Test. Learn. Grow.
Optimizing your landing page with experimentation and personalization
A/B testing server-side with full stack
Why Apple ITP is a threat for 30% of your traffic—how can you trust your future experimentation insights?

4 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 approach 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 until the end of the test.

Bayesian approach

This approach provides a result probability when 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.

Further reading:

A/B testing: What traffic volumes do you need for reliable results?
Bayesian or frequentist statistics: which method should you use for your A/B tests?
Are you stopping your A/B tests too early?
Understanding the statistical value of an A/B test

5 A/B testing : Full stack or client-side

The best approach to choose will depend on company structure, internal resources, the development life-cycle and the complexity of the experiments, as this blog will explain.

Client-side experimentation and personalization does not require advanced technical skills, making it well-suited to digital marketers. It enables teams to be agile and to run experiments very quickly, avoiding bottlenecks and getting faster test results.
Server-side testing approach requires technical resources and more complex developments, it does enable more powerful, scalable and flexible experimentation.

Brands must be able to use each of these approaches to involve all their teams in the optimization process and manage their different projects in the best conditions.

The client-side approach: increased flexibility for marketing teams

In a client-side environment, web pages are modified directly in the visitor’s browser. Essentially, the source code of the original page is forwarded from the server to the end user’s browser, and a script steers all the changes to the browser (whether it is Chrome, Firefox, Safari, Opera, etc.) on the fly to display a version of the modified page.

With client-side testing, you can create and deploy front-end tests and personalizations very quickly. For example, you can update text elements, change the placement of CTA buttons or content blocks, or add pop-ups to improve usability.

The limitation of client-side testing is that experimentation possibilities are not as vast as for server-side testing, which can include testing for features, back-end logic, and algorithms.

Server-side testing approach: maintain control over your experiments.

Working server-side means that optimization hypotheses are created on the back-end architecture side and not via the visitor’s browser, as is the case with the client-side approach. The changes are directly generated when the HTML pages load.

In server-side testing, you control all the elements of your tests and experiments from within the coding environment, directly. Consequently, you can run more in-depth tests and personalizations on your sites, and with more freedom.

One catch with running server-side tests in experimentation platforms is that non-technical users often have to trade off user-friendly capabilities, like a no-code, graphic editor. They also have to rely on their Developers to configure and maintain integrations to third-party tools if they want to have precise audience targeting using external segments or wish to sync experimentation data to other platforms.

Hybrid experimentation: bringing together client and server-side testing

There is a third option that allows experimenters to enjoy client-side capabilities even when running tests server-side. In Hybrid experiments, users get the best of the two approaches. Marketers, Product Managers and Developers can work in a shared platform, using tools that are familiar to them, and without the need for Developers to code and maintain integrations to external tools. With Hybrid experimentation, users don’t have to choose between a client-side or server-side approach and settle for trade-offs that impede their efficiency and hinder cross-team collaboration around experimentation.

6 How to put in place an A/B testing strategy

A/B testing must be based on rigorous methodology to obtain relevant results and implement practices that let teams move forward and learn from the experiments launched on the website. Here are 5 stages for putting your testing strategy in place:

1. Measure and analyze your website performance to identify what can be optimized

Before optimizing the visitor experience and redesigning your website, it is essential to identify the weak points and areas to optimize on your pages.
Every website is different and brands must develop their strategy based on the nature of their audience, their goals and the results obtained after analyzing their website performance.
To identify friction points on your website, you can use behavioral analysis tools such as click tracking, heatmaps, etc.

2. Formulate your optimization hypotheses

Once you’ve identified the friction points that are stopping your visitors from converting on your website, formulate hypotheses to figure out what experiments to put into place.

For example:

Observation: the sticky bar you installed is only rarely used by website visitors.
Hypothesis: perhaps the icons are not clear enough; adding information could improve this weak point.
Planned experiment: adding wording below each icon.

3. Prioritize your A/B tests and establish your roadmap

To implement an effective A/B testing roadmap and obtain convincing results, it is vital to prioritize your actions. With the Pie Framework, created by WiderFunnel, you can rank your test ideas according to three criteria rated 1 to 10 to determine where to start:

Potential: on a scale of 1 to 10, how much do you think this page can be improved?
Impact: what is the value of the traffic (volume, quality) on this page?
Ease of implementation: how easy is it to implement the test (10 = very easy, 1 = very difficult)?

By averaging the three grades, you’ll know which tests to launch first. (There are, of course, other prioritization frameworks; feel free to find the one you prefer.)

4. Analyze your A/B tests and learn from your results

It’s crucial to analyze and interpret your test results. After all, A/B testing is all about learning and making decisions based on the analysis of your experiments.

For effective analysis of your results:

Learn to recognize “false-positives”
Establish representative visitor segments
Don’t test too many variations at the same time
Don’t give up on a test idea after one failure

7 What elements should you A/B test on your website?

You can A/B test absolutely everything on your website, from messages to design to browsing elements. Here are a few examples to inspire you: