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Product-AB-Testing

Introduction

This project is focused on GloBox, an e-commerce enterprise recognized for its boutique fashion items and premium decor products. Nevertheless, the company has witnessed substantial growth in its food and beverage offerings over recent months. To capitalize on this expansion and amplify revenue, the company seeks to raise awareness of this product category. Consequently, an A/B test has been proposed to spotlight food and beverage products through a banner. The primary objective of this test is to gauge its influence on user behavior, with a specific emphasis on purchases and conversions.

Comparative Analysis of User Spending in A/B Testing

In this project, I conducted a detailed examination of the average amount spent per user in both the control and treatment groups, employing a statistical approach with a focus on confidence intervals and hypothesis testing.

95% Confidence Interval for Control Group:

  • Utilized the t-distribution to calculate a 95% confidence interval for the average amount spent per user in the control group. This interval provides a range within which we are 95% confident that the true average spending per user lies.

95% Confidence Interval for Treatment Group:

  • Similarly, applied the t-distribution to establish a 95% confidence interval for the average amount spent per user in the treatment group. This interval serves as a robust estimate, considering the variability in user spending.

Hypothesis Test for Group Comparison:

  • Conducted a hypothesis test to ascertain whether there is a significant difference in the average amount spent per user between the two groups. Employed the t-distribution and assumed unequal variance, setting a 5% significance level.

  • The resulting p-value from the hypothesis test was analyzed to draw conclusions regarding the presence or absence of a statistically significant difference in spending patterns between the control and treatment groups.

This analytical approach provides a comprehensive understanding of the spending dynamics in the A/B testing scenario, offering insights into the confidence intervals and statistical significance of observed differences between the two user groups.

Final Report:
https://github.com/Lexusr2020/Product-AB-Testing/blob/main/Mastery%20Project%201%20Report.pdf

Video Presentation:
https://drive.google.com/file/d/1Y8dB8MK03kESXs2Z9ZQ7zC2dcpxqL8ok/view?usp=drive_link

Tableau:
https://public.tableau.com/app/profile/lex.romo/viz/MasteryProject1_2_0/Story?publish=yes

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