A/B Testing Marketing Campaign Analysis

Project Overview

This project analyzes marketing campaign performance using A/B Testing and statistical hypothesis testing to determine which campaign variant delivers better conversion rates and customer engagement. The analysis helps businesses make data-driven marketing decisions.

Objective

Compare two marketing campaign variants.
Measure campaign effectiveness using statistical methods.
Validate hypotheses with significance testing.
Recommend the best-performing campaign based on data.

Technologies Used

Python
Pandas
NumPy
SciPy
Matplotlib
Jupyter Notebook

Key Features

Data preprocessing and cleaning.
Exploratory Data Analysis (EDA).
Statistical hypothesis testing.
A/B test implementation.
Conversion rate comparison.
Data visualization and business insights.

Analysis Performed

Compared conversion rates between Campaign A and Campaign B.
Conducted statistical significance testing.
Visualized campaign performance using charts.
Evaluated confidence levels and p-values.
Identified the winning marketing strategy.

Skills Demonstrated

A/B Testing
Hypothesis Testing
Statistical Analysis
Python Programming
Data Visualization
Business Analytics
Decision Making

Outcome

Successfully identified the better-performing marketing campaign using statistical analysis, enabling data-driven optimization of marketing strategies and demonstrating practical applications of A/B testing in real-world business scenarios.