Why Should You Consider A/B Testing?

What is A/B Testing?

A/B testing also known as bucket testing or split testing is a method in which you take a look at two unique variations of a website or app to see which one gets higher results....

Why Should You Consider A/B Testing?

Below are some of the reasons to consider A/B testing a webpage:...

What Can You A/B Test?

A/B testing is a versatile tool that can applied to different domains. Here are some examples of what you can A/B test on a website:...

Types of A/B Testing

Below are the different types of A/B testing:...

Statistical Approach to use to Run A/B Test

Below is the comparison between two statistical approaches Frequentist approach and the Bayesian approach:...

Steps to Conduct an A/B Test

A/B checking out is a technique used to evaluate distinct versions of a website or internet web page to determine which one performs higher. The intention is to optimize the website for precise objectives, including increasing conversions, income, or engagement. Below are the steps you must follow when conducting an A/B check:...

A/B Testing Process

...

What are Variant A and Variant B?

Variant A is the original model (additionally called the manipulate), and Variant B is the new edition which you’re checking out....

What is the Conversion Rate?

This is the percentage of customers who take a particular movement (like making a purchase) out of the overall wide variety of visitors....

What do you mean by Statistical Significance?

Statistical significance refers to the level of confidence that the differences you observe in the test are not due to chance....

Mistakes to Avoid While A/B Testing

Invalid Hypothesis: In A/B testing all the steps depend upon the hypothesis developed before beginning the test. A hypothesis involves what should be changed, why it should be changed, and the expected outcome. If a test starts with a wrong hypothesis, the probability of a successful test is very low. Testing Wrong Page: Split testing wrong pages can waste time and valuable resources. It is important to determine what to test and identify the best pages to test that will increase the conversion. Testing Too Many Elements Together: Testing too many elements together makes it difficult to pinpoint which element influenced the test’s failure or success. Prioritizing tests is important for successful A/B testing. Working with Wrong Traffic: The site must have a healthy amount of traffic to its pages. If the site has heavy traffic then the split tests will be completed relatively faster in comparison to when there is low traffic then tests need to be executed for a longer period. Running Split Test at Wrong Time: To split test a website, it is important to determine the correct timing. If a page gets most of its traffic on Friday then it does not make sense to compare the test results of Friday with low-traffic days. Running Tests for Not Long Enough: To achieve statistically significant test results it is important to run tests for a certain amount of time. Using Wrong Tools: There are multiple low-cost tools available in the market for A/B testing. Not all the tools are equally capable and not all tools provide all the necessary features. Some of the tools can slow down the site leading to data deterioration. Using faulty tools can affect the test’s success. Measuring Results Inaccurately: Measuring tests accurately is equally important as conducting tests accurately. If the results are not measured correctly then one cannot rely on the data. Running Tests on Wrong Site: Sometimes the split tests are being conducted on development sites instead of the live sites. It is important to switch the tests from development sites to live sites as development sites are used by developers, not customers. Not Documenting: Documenting every detail is important. Some companies skip this step or the documentation is not in one place, it is scattered across multiple emails. When there is a need to determine why the change was done, it becomes difficult to trace back the details of the change....

Challenges in A/B Testing

Generating Required Sample Size: If the website receives lower traffic then it will be difficult to reach the required sample size. To get conclusive results the duration of the test needs to be increased to collect the sample size for the test. Deciding What to Test: It is challenging to decide what to test on the website as not every small change that is easy to implement is best for the business goals and the same goes for the complex tests. Website data and visitor analysis data help to determine what to test. Developing Hypothesis: Formulating a hypothesis is a challenge as it depends upon the accuracy of the collected data. With the help of the data gathered in the first step of the A/B testing, it needs to be determined where the problem lies and what needs to be addressed. Handling Failed Tests: When tests fail, the best option is to keep trying the permutations and land on the right page with the right combination of elements, and get the desired results. Flicker Effect: Flicker effect means when the original page appears before the user for an instant before the variation is displayed. It can affect the test results due to poor user experience....

A/B Testing and SEO

Below are some of the best practices to avoid bad effects on Google search behavior:...

Conclusion

A/B testing is a powerful tool that provides various benefits like improved user experience, supporting data-driven decision-making, increased ROI, and so on. By implementing A/B testing organizations can make informed and statistically effective decisions ultimately leading to better outcomes....

FAQs

1. What do you mean by Control Variant?...