This project performs a comprehensive A/B Testing-based statistical analysis of a marketing campaign to determine whether a personalized strategy is more effective than a generic approach. The analysis is done using Python and essential data science libraries, with detailed steps from data loading and cleaning to hypothesis testing and final business conclusions.

The primary goal of this project is to:

• Evaluate the performance of two marketing strategies: Control (generic message) and Personalization (customized message).
• Use statistical testing (A/B testing) to determine if there is a significant difference in conversion rates between the two.
• Derive actionable insights for the marketing team based on statistical evidence.

The dataset simulates responses to two marketing campaigns sent to different customer segments.

• Features include:
  • user_id: Unique identifier for each participant.
  • group: Control or Personalization.
  • converted: Binary value indicating whether the user converted (1) or not (0).

The project follows a structured data science approach:

• Load the dataset.
• Check for missing or duplicate values.
• Perform basic statistics on conversion rates.

• Visualize conversion distributions between control and personalization groups.
• Use bar plots and boxplots to compare performance.

• Null Hypothesis (H₀): There is no significant difference in conversion between Control and Personalization.
• Alternative Hypothesis (H₁): Personalization leads to higher conversion rates.
• Perform a two-sample t-test (independent) to validate hypotheses.

• Measure the lift in performance.
• Report p-values and statistical significance.

• Conversion Rate (Control) for dependent factors
• Conversion Rate (Personalization) for dependent factors
• Lift: for dependent factors
• Conclusion: The personalization campaign performs significantly better than the generic campaign for each segment ( Email, Instagram, House ads, etc.)

• Python
• Pandas – data manipulation
• Matplotlib & Seaborn – visualization
• NumPy – numerical operations
• SciPy – statistical testing

• A/B Testing
• Experimental Design
• Hypothesis Testing
• Data Cleaning & EDA
• Statistical Significance Analysis
• Visualization & Interpretation

• Personalized marketing increases conversion and should be prioritized.
• A data-driven approach can guide campaign decisions and increase ROI.

1. Clone the repository:

   git gh repo clone suryamr2002/Marketing-Campaign-A-B-Testing

2. Launch Jupyter Notebook and open the .ipynb file:

   jupyter notebook

Author: SURYA M.R 📧 LinkedIn
📂 GitHub Portfolio