What is A/B Testing in Data Science?

A/B testing involves comparing the effectiveness of two versions of the same content with users. The goal is to identify the version of the program, web page, email… that generates the most web traffic. It provides the original version or “control version” and the modified version.

It allows assess the relevance of the improvement and its impact on user behavior. In principle, the efficiency of the new version should translate into a better user experience and improved conversion rate.

Database preparation

Data processing

Before performing an A/B test, it is important to have a reliable and consistent database. This guarantees the accuracy of the results and the relevance of the interpretations. Therefore it is necessary merge, correct or even remove corrupted data or not relevant.

In addition, recently updated data guarantees the reliability of the results. Indeed, user behavior is constantly changing and statistics vary from one period to another. Updated information makes it particularly easy to select a target audience. It can be prospects, current users… This step is as important as sampling.

Control version

Before conducting an A/B test, you need to have a clear understanding of how the control version works. The conversion rate of web pages or paid advertising campaigns will be used as a reference in the analysis of the results. Know the features of version A it retains its importance for the rest of the process. This allows you to identify the points that need to be improved and the strong points to increase the traffic on the site.

Tuning test software

To guarantee the reliability of the results, it is important to ensure the homogeneity of the behavior of the target audience. It also happens that the software used for the test affects the process. Therefore, A/A testing is needed to anticipate such events. consists of submit the same page separately to observe user behavior. In principle, the results should be similar. In the event of a significant deviation, a correction in the database or test program is essential.

Reliability

A/B test results

Confidence index

Test results also have a “confidence index” or “confidence level.” It shows the validity of A/B test results. It considers the statistical representativeness of the test and assesses the likelihood that the results will be replicated in reality. Generally takes A confidence index greater than or equal to 95% to ensure the validity of the text.

Statistical power

Regarding the duration of the test, it depends on the size of the sample. Generally, it takes at least three weeks to confirm the results of an A/B test. This minimum period makes it possible to give it Statistical power greater than or equal to 80%.

Several factors determine the statistical power of an A/B test:

• The sample size consists of the number of visitors. The higher the traffic, the more reliable the test.
• The difference between the conversion rates of the control version and version B. If the difference between the two versions is small, a larger sample is needed.
• Statistical representation.

Interpretation

A/B test results

Null hypothesis

The interpretation of the results allows to use the test and evaluate the relevance of improving the existing system. It happens Pages A and B record roughly the same performance. In this case, changing the marketing medium or page has no effect on user behavior. This is called the null hypothesis or H0.

An alternative hypothesis

On the other hand, an alternative hypothesis is that Page B has a higher conversion rate than Page A. In other words, changing one or more variables prompted users to take action. .

Let’s take the example of resizing a button

call to action. If the hypothesis has no effect, it is null

click through rate. On the contrary, a assumption

It is an alternative if page B has a higher click-through rate to the control version.

A null hypothesis in no way implies a process failure

increasing site traffic. On the contrary, it removes traces

and reduces opportunities.

Practical case

Regardless of their activities, all companies with a presence on the Internet can use A/B testing to improve traffic on their sites. B2C companies that sell their products online have interest test two different versions of the call to action. It helps you approve or reject leads to improve click-through rates.

B2B companies also benefit from using A/B testing to improve their performance. In particular, they can test prospecting emails to find the formula that generates the most conversions.