About A/B Tests: 10,000 Facebook Versions, How to Generate Ideas, How to Conduct, What Not to Do, Useful Frameworks

A/B testing is a powerful tool for marketers and website owners because it allows them to make data-driven decisions on how to improve their websites. By comparing two versions of a website, A/B testing allows marketers to determine which version performs better in terms of engagement, conversion, or other important metrics.

This allows them to make informed decisions about how to improve their website and ultimately increase conversions and revenue. It’s about finding growth points, and it’s one of the basics of business growth.

Big companies like Facebook, Google, and Amazon approach A/B testing in a very methodical and data-driven way. They usually have large teams of data analysts and engineers responsible for running A/B tests and analyzing the results. They also have access to large amounts of data and advanced tools that allow them to run large-scale A/B tests with a high degree of accuracy.

For example, Facebook is known for its extensive A/B testing practices. They run thousands of tests every year to improve their products and have a dedicated team of engineers responsible for running and analyzing the results of these tests. This allows them to make data-driven decisions on how to improve their products and ultimately increase user engagement and revenue.

In an interview, Mark Zuckerberg admitted that FB’s success lies in their unique testing system, which the entrepreneur is very proud of: “At any given moment, there is not one version of FB running in the world but about 10,000.”

Google also has a large team of analysts and engineers who run A/B tests on various aspects of their products, such as search results, ad placement, and landing pages. This allows them to constantly improve their products and maintain their position as one of the most popular search engines in the world.

Amazon is another company that conducts extensive A/B testing. They test everything from product pricing to landing pages to make sure they are giving their customers the best possible experience.

Jeff Bezos once said: “Amazon’s success depends on how many experiments we can run per year, per month, per week, per day.”

So, A/B testing is a cool way that allows companies to make data-driven decisions on how to improve their websites and products. Big companies like Facebook, Google, and Amazon approach A/B testing in a methodical and data-driven way, using large teams of analysts and engineers, as well as large amounts of data and modern tools to run large-scale A/B tests with a high degree of accuracy.

How to generate ideas for A/B testing on your website

1. Analyze your data: Take a look at your website analytics to identify areas where users are bouncing or encountering problems. These areas can provide insight into what you need to test.
2. Look at your competitors: Analyze your competitor’s websites to see what they are doing well and what they are doing wrong. You can use this information to come up with ideas for tests on your website.
3. Look at websites from related and completely different niches: Sometimes the eye gets blurred because a niche usually has certain standards and foundations that have not changed for years. That is why it is important to look at what is happening in other niches. Believe me, sometimes, you can find very cool things.
4. Get feedback from users: Ask users to leave feedback about your site. You can do this through surveys, user testing, or focus groups. Use this feedback to identify areas of your website that need improvement and come up with ideas for tests.
5. Use heat maps: Use heatmap tools to understand how users interact with your site. This can help you identify areas of your website that need improvement and come up with ideas for tests.

Here are some tools for this: Hotjar, Lucky Orange, and Mouseflow.

1. Brainstorming: Gather a group of people, including designers, developers, and other marketers, and brainstorm ideas for tests.
2. Use best practices: Check out industry best practices and see how you can apply them to your website.
3. Test different elements: Test different elements of your website, such as headlines, images, calls to action, layouts, and color schemes. Break your page down into small parts and try to think about changing a particular element. You might be surprised, but even a small element can affect conversions.
4. Test different goals: Test different goals, such as increasing conversions, enhancing engagement, or decreasing bounce rate.

It’s important to remember that A/B testing is a cyclical process, so you should constantly test and improve your site to boost its performance.

How to conduct A/B tests

A/B testing is great, but it’s important to do it right to avoid skewing results and making decisions based on inaccurate data. By following even these simple steps listed below, you can be more confident that your A/B testing is done correctly and that the results will be accurate and useful for decision-making.

1. Define the purpose of the test: Before you start testing, you should have a clear idea of what you hope to achieve with the test. It could be increasing conversions, improving engagement, or reducing bounce rates.
2. Select the elements to test: Decide which elements of your website you want to test. These can include headlines, images, calls to action, layouts, and color schemes. It’s best to test one variable at a time to ensure accurate results.
3. Create a hypothesis: Once you’ve determined the purpose of the test and the elements you want to test, create a hypothesis about what results you expect.
4. Determine the sample size: Determine how many visitors will be included in the test. A/B tests require a large sample size to ensure accurate results. Calculators will help you with this, and there are quite a few of them: https://www.abtasty.com/sample-size-calculator/, https://www.evanmiller.org/ab-testing/sample-size.html.
5. Choose the duration of the test: Decide how long the test will last. A/B tests should last for a week or two or even longer. It’s more accurate to say that several business cycles should pass. Take into account holidays, weekends, and other events that may affect the test.
6. Set up the test: Once you have a plan, set up the test using tools like Google Optimize or Optimizely.
7. Monitor the test: Throughout the test, monitor the process to make sure everything is running smoothly and there are no technical issues.

Analyze the results: Once the test is complete, analyze the results and make decisions based on the data.

1. Take action: Based on the test results, decide which version of the site or page to implement next.
2. Conduct A/A testing: It is conducted to make sure that the A/B testing process is working correctly. During the A/A test, the same version of the page is shown to two different groups of users. If the results are not statistically significant, it means that the testing process is working correctly.
3. Test constantly: A/B testing is a cyclical process, so it’s important to constantly test and refine your site to improve its performance.

The most common mistakes when conducting A/B tests

A/B testing is a powerful tool, but it is important to use it correctly to avoid distorting the results and making decisions based on inaccurate data and, as a result, business losses.

8 anti-tips for conducting A/B tests, or what you should not do:

Testing too many variables at once: When testing several elements of your website, it can be difficult to determine which particular change led to a certain result. It’s best to test one variable at a time to ensure accurate results. You can skip this step when you need to get results quickly and figure it out later, but it will be difficult.

Insufficiently large sample size: A/B tests require a large sample size to ensure accurate results. If your sample size is too small, you won’t be able to draw accurate conclusions from the data. This is often the main problem with curve tests.

Insufficiently long testing period: It’s important to run tests for a long period to ensure that the results are accurate. A/B tests should last at least a week and preferably longer.

Not using a statistical significance calculator: It’s important to use a statistical significance calculator to ensure that your test results are statistically significant and not just random.

Lack of a clear hypothesis: Before you start testing, you should have a clear hypothesis about what results you expect and what you hope to achieve with the test. This will help you interpret the results correctly. Many people fail here.

Not taking external factors into account: A/B testing should be conducted in a controlled environment free from external factors that can affect the results, such as holidays or special events.

Not acting on the results: After testing, it’s important to analyze the results and make decisions based on the findings. Failure to act on the findings is a waste of time and resources.

Failure to monitor the test: It’s important to monitor the test throughout its duration to ensure that everything is running smoothly and that no technical issues arise.

Everything looks simple, but in reality, there are always many details related primarily to the technical skills of the team, websites and CMS, analytics, and proper configuration.

Frameworks for A/B testing

Many frameworks can be used for A/B testing, each with its strengths and weaknesses. Frameworks help to prioritize, systematize, and optimize the A/B testing process. Sooner or later, everyone comes to them.

Remember a golden rule: each framework should include basic steps: creating a hypothesis, designing an experiment to test the hypothesis, collecting data, and analyzing the results. This principle provides a structured approach to testing and data-driven decision-making.

And now about the frameworks:

I will write about 2 main ones + 1 hybrid

PIE Framework: This framework is used to prioritize website elements for testing. The PIE framework stands for “potential, importance, and ease.” Potential means the potential impact of the change, importance means how important the change is, and ease means how easy it is to implement the change.

If you’re just starting out, PIE is right for you.

VICE or ICE system: This system is used to assess the potential impact of changes. The VICE framework stands for “velocity, impact, confidence, ease.” Velocity refers to the speed at which the test can be performed, impact refers to the potential impact of the change, confidence refers to how confident you are about the change, and ease refers to how easy it is to implement the change. I’ve created a basic template for you, which you can download from GoogleSheets.

The VICE system is very common, but it is often customized and modified, leaving the basic things but adding something else.

PXL framework: I recommend reading the source https://speero.com/post/how-to-prioritize-your-a-b-tests-ideas

If you don’t want to use frameworks, if you think it’s too much, you may be right. But before you start testing, you have to answer 5 simple questions.

1. What is the purpose of your test, and why is it important? What indicator do you want to influence, or what goal are you pursuing?
2. What do you expect and why? Why it will affect, describe the hypothesis.
3. How will version A differ from version B? Describe the changes.
4. What will you measure and how will you measure it? What metrics will you look at, and what tools will you use?
5. What do you need to do to implement the changes? Describe the entire work plan.
6. How much traffic do you need? Calculate it with a calculator, for example, https://www.evanmiller.org/ab-testing/sample-size.html 

Conclusion

A/B tests should be a standard task rather than something remarkable. Yes, it is difficult, and yes, it is important to prepare and conduct it properly, but it should be routine. Invest in the knowledge and skills that help you conduct testing properly.

Useful resources: 

1. https://www.dynamicyield.com/learning-paths/
2. https://unbounce.com/resources/#!landing-page-basics
3. https://cxl.com/blog/category/cro-testing/
4. https://conversion-rate-experts.com/articles/
5. https://www.abtasty.com/blog/