Marketing Campaign A/B Testing 101

Discuss the concept of A/B testing, explore alternative approaches when traditional A/B testing is impractical, and delve into its application for enhancing marketing campaigns.

Elaborate on statistical aspects relevant to A/B testing, including the significance of statistical power, and how different factors impact statistical power and sample size.

Overview of the A/B Testing Procedures

How to formulate the problem statement for an A/B testing experiment

Clarify the process of selecting a success metric for an A/B test by considering multiple criteria. By the end of this lesson, you will possess a comprehensive grasp of identifying and gauging success metrics in your own projects.

Explain how to formulate hypotheses for statistical analysis, and will cover the four parts of formulating a hypothesis, including the hypothesis statements, the significance level, the statistical power, and the MDE.

Explain the content of the experimental design, emphasizing the significance of establishing the randomization unit and defining the target population for the experiment.

Illustrate the process of establishing and executing an experiment, emphasizing the value of cooperation with Data Engineering and Software Engineering teams and outlining their responsibilities, which include configuring test pipelines and generating tables.

Describe the concept of Validation Checks, encompassing the AA Test, SRM, and Novelty Effect.

Examine the interpretation of A/B testing outcomes by considering the utilization of p-values and confidence intervals.

Deliberate on the decision to launch upon their statistical significance and its practical implementations.

Congratulations to all participants for successfully finishing the course, and express gratitude for placing your trust in Datanough. If you're keen to explore further topics like Python and A/B Testing Interviews, feel free to visit our Datanough Academy website.