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 determine which version of the application, web page, email… generates the most web traffic. Offers the original or “control copy” and the modified version.

This allows Evaluate the appropriateness of the improvement and its impact on user behaviour. In principle, the efficiency of the new version should translate into a better user experience and an improved conversion rate.

Database processing

Data processing

Having a reliable and consistent database is essential before conducting an A/B test. This ensures the accuracy of the results and the relevance of the interpretations. must therefore Merge, correct, or even remove corrupted data or irrelevant.

In addition, recently updated data ensures the reliability of the results. In fact, user behavior is constantly changing and Statistics fluctuate from period to period. The updated data especially facilitates the selection of the target audience. It could potentially be, existing users… This step is just as important as sampling.

control copy

It is essential to clearly understand the performance of control copy before conducting A/B testing. The conversion rate of web pages or paid advertising campaigns will be used as a reference in analyzing the results. Learn about the features of version A It carries its importance in the rest of the process. It makes it possible to identify points that need to be improved and strong points to take advantage of to increase website traffic.

Modify the test program

In order to ensure the reliability of the results, it is important to ensure the homogeneity of the target audience’s behavior. It also happens that the software used for testing affects the process. Therefore A/A testing is necessary to anticipate such possibilities. It consists of Submit the same page separately To monitor user behavior. In principle, the results should be similar. If a significant deviation occurs, it is necessary to make an adjustment in the database or test program.


A/B test results

confidence index

Test results also have a “confidence index” or “confidence level”. It indicates the reliability of the results of an A/B test. It takes into account the statistical representation of the test and evaluates the probability that the results will actually reproduce. generally takes confidence index greater than or equal to 95% To ensure the credibility of the text.

Statistical power

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

Several factors determine the statistical strength of A/B testing:

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


A/B test results

null hypothesis

Interpretation of the results makes it possible to benefit from the test and assess the suitability of improving the existing system. That happens Pages A and B perform roughly the same. In this case, modifying a marketing medium or page does not affect user behavior. This is called the null hypothesis, or H0.

alternative hypothesis

On the other hand, it is considered an alternative hypothesis if page B has a higher conversion rate than page A. In other words, modifying one or more variables prompts users to take action.

Let’s take the example of resizing a button

call to action. The hypothesis is null if it has no effect on

click rate. On the contrary, it is a file hypothesis

Alternative if page B has a higher CTR to copy control.

A null hypothesis in no way means that a process has failed

Increase site traffic. On the contrary, it rejects the tracks

reduce the odds.

practical case

Whatever their activities, all businesses on the web can use A/B testing to improve their site traffic. B2C companies that sell their products online are interested in Test two different versions of your call-to-action. This helps confirm or exclude leads to improve click-through rates.

B2B companies also benefit from the use of A/B testing to enhance their activities. In particular, they can test your prospecting emails to find the formula that generates the most conversions.

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About the Author: Irene Alves

"Bacon ninja. Guru do álcool. Explorador orgulhoso. Ávido entusiasta da cultura pop."

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