Email Marketing Campaign Analysis

Project Overview

This project analyzes the effectiveness of two distinct email marketing campaigns for an e-commerce company, identified as "Campaign A" and "Campaign B". It compares traditional email content with interactive and personalized content to determine which is more effective in engaging customers.

Features

• Analysis of email open rates, click rates, and purchase amounts for each campaign.
• Statistical testing to evaluate the significance of differences between the campaigns.

Technologies Used

• Python
• Pandas for data manipulation
• SciPy for statistical tests

Getting Started

Prerequisites

• Python 3.x
• Pandas
• SciPy

Installation

1. Clone the repository:

2. Install required libraries:

Usage

Execute the main script from the project directory:

Data

Place the email_marketing.csv dataset in the Data folder. The dataset should include the campaign type, email open rate, click rate, and purchase amount columns.

Detailed Instructions

1. Install Only Required Libraries and Tests

   • Ensure that only the necessary libraries (pandas, scipy) and any specific tests you plan to conduct are installed in your working environment.

2. Load the Dataset

   • Load email_marketing.csv into your working environment to begin analysis.

3. Initial Impressions from the Data a. Display the first and last 10 rows of the dataset to get an overview. b. Comment on any notable aspects within the groups you are comparing, such as average values.

4. Conduct Statistical Tests at 1% Significance Level a. Determine the assumptions that need to be tested to decide on the correct statistical test. If unnecessary, skip testing the assumption. b. Perform the final tests and display the results. c. Interpret the business problem considering the final test results.

5. Re-evaluate Results at 5% Significance Level

   • Reinterpret the results obtained in question 4 at a 5% significance level. Only repeat the tests you deem necessary, or copy the results from question 4 and add your comments below.

Results

The analysis results, including statistical tests, will be printed to the console. Use these results to make informed decisions about email marketing strategies.

Contributing

Feel free to fork the repository, make changes, and submit pull requests. You can also open issues to discuss potential improvements or report bugs.

License

This project is licensed under the MIT License.