https://github.com/zborovskaanna/ad-ab-test-analysis

Analysis of Ad AB Test results using Python. Conducted comprehensive data analysis, including sanity checks, to evaluate ad campaign performance and derive key insights
https://github.com/zborovskaanna/ad-ab-test-analysis

ab-testing analysis hypothesis-testing marketing-analysis pandas python sanity-checks scipy-stats seaborn

Last synced: about 1 month ago
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Analysis of Ad AB Test results using Python. Conducted comprehensive data analysis, including sanity checks, to evaluate ad campaign performance and derive key insights

• Host: GitHub
• URL: https://github.com/zborovskaanna/ad-ab-test-analysis
• Owner: zborovskaanna
• Created: 2024-07-31T08:46:59.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-31T13:05:50.000Z (5 months ago)
• Last Synced: 2024-08-01T15:52:53.471Z (5 months ago)
• Topics: ab-testing, analysis, hypothesis-testing, marketing-analysis, pandas, python, sanity-checks, scipy-stats, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 613 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Analysis of Ad AB Test Results  

This project evaluates the effectiveness of a creative, interactive online advertisement for the SmartAd brand by comparing it to a standard control ad.  

The goal is to determine whether the creative ad leads to higher user engagement.  

### Key Findings  

- User Interaction: Users interacted more with interactive ads, with higher frequencies of "yes" or "no" responses.

- Statistical Significance: A significant difference in the number of "yes" responses to interactive ads was observed.

- Distribution Issues: Imbalances in user distribution by browsers, operating systems, and devices were noted. Ad impressions were uneven, though user ratios between groups were correct.

### Recommendations  

- Follow-Up Test: Conduct with improved technical implementation, ensuring even ad impressions and balanced data distributions.

- Extended Duration: Extend the test duration to two weeks to identify weekly and hourly variations for better ad budget optimization.

### Contents  

- Description

- Exploratory Data Analysis (EDA)

- Analysis of Ad Impressions and User Interactions

- Sanity Checks

- Sample Ratio Mismatch

- Distributions Balance

- Hypothesis Testing

- Conclusions and Recommendations

### Tech Stack  

Python

Pandas

Seaborn

scipy.Stats