A/B test sandbox via Lookerstudio

In typical business operations, achieving objectives often involves investing money in marketing. The marketing division establishes ideas and an action plan to align with overall business objectives. However, for many of these ideas, it's challenging or even impossible to assess their performance and effectiveness for the organization in advance. In such a scenario, a practical approach emerges: what if we test our ideas not on all our clients but on a segment of them? If we observe positive results, then we can apply these successful ideas to all clients.

AB testing offers this capability. You can forecast the effectiveness of ideas with a certain percentage of accuracy, also known as a p-value. However, there are limitations to verifying any idea using AB test.

• Team Cost: The expense associated with planning, executing, and analyzing AB tests.

• Time Spent to Conduct: AB testing can be time-consuming. It depends on confident level, sample size, and differences should be detected

• Risk of Being Mistaken: The potential cost and consequences of making incorrect decisions based on test results.

To address these limitations, we can adopt a modeling approach. This will provide more insights into how tests can progress, indicating the necessary time and sample size for launching tests. Additionally, we can simulate real processes, uncovering new ideas without investing a significant amount of time in actual tests.

Implementation

prepare dataset that will be filled with data we want to modeling

Code illustrating modeling situations and generating samples should populate the dataset.

Link the dataset to Looker Studio, and now we can observe how our modeled situation will develop and the potential figures we can obtain.

https://lookerstudio.google.com/reporting/7bcc9aa2-9ead-4047-8f37-45fba2cc0b56/page/Q6rnD

AB test playground can became a powerful tool for you marketing team and help you with

• demonstrate ab test process and it results

• searching new ideas for experiment

• modeling real processes

• prepare good designed experiments to launch

• calculate sample size and way to optimise it