A/B Testing of Marketing Campaigns

Project Description

This project focuses on conducting A/B testing to evaluate the effectiveness of two marketing campaigns. Using statistical analysis and hypothesis testing, we determine which campaign is more effective in improving conversion rates.

Project Structure

The project consists of three main parts, each represented in a corresponding Jupyter Notebook:

1. 01_data_preprocessing.ipynb: Data loading and preprocessing.
2. 02_stat_analysis.ipynb: Statistical analysis and visualization of results.
3. 03_conclusion.ipynb: Conclusions and recommendations based on the analysis.

Files

• datasets/control_group.csv: Data for the control group.
• datasets/test_group.csv: Data for the test group.
• datasets/control_i.csv: Processed data for the control group.
• datasets/test_i.csv: Processed data for the test group.
• functions.py: File containing functions for data processing and analysis.

Key Skills

• Data Processing: Using Pandas and NumPy libraries for data loading, cleaning, and preprocessing.
• Statistical Analysis: Performing hypothesis testing (t-test) to assess statistical significance between groups.
• Data Visualization: Utilizing Matplotlib and Seaborn libraries to create graphs and visualize results.
• Data Consistency Checks: Verifying data for logical consistency and correcting anomalies.

Findings

Based on the analysis, we concluded that the new (test) marketing campaign is more effective compared to the control campaign. Key insights include:

• The conversion rate for the control group was 1.23%, while for the test group, it reached 2.54%.
• The test group was 2.07 times more effective than the control group.
• Statistical significance was confirmed via a t-test, showing a p-value of 0.001, indicating that the improvement in conversion rate is not random. .

Recommendations

Based on the results, it is recommended to adopt the test campaign as the primary marketing strategy to optimize user engagement and improve conversions.

How to Run the Project

1. Clone the repository:

   git clone https://github.com/ituvtu/DataMining-AB-Testing

2. Install the required libraries:

   pip install -r requirements.txt

3. Launch Jupyter Notebook and open the following files for execution and analysis:

• 01_data_preprocessing.ipynb
• 02_stat_analysis.ipynb
• 03_conclusion.ipynb

Author

ituvtu (LinkedIn).

This project is licensed under the MIT License.