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K-Means Clustering using R for E-commerce Customer Segmentation

Overview

This project utilizes K-means clustering in R to analyze e-commerce customer data and create meaningful customer segments. Leveraging the RFM (Recency, Frequency, Monetary) technique, the project categorizes customers into distinct clusters, providing actionable insights for marketing strategies.

Methodology

Data Analysis

• Explored and analyzed the e-commerce customer data to understand trends using libraries such as ggplot, dplyr, factoextra, reshape2, etc.
• Employed the elbow method to determine the optimal number of clusters for segmentation using K-means.

Customer Segmentation

• Identified distinct customer segments:
  • Cluster 1: New/Passing By (Silver)
  • Cluster 2: Best Customers (Platinum)
  • Cluster 3: Loyal Customers (Gold)
  • Cluster 4: Losing Customers (Bronze)

Recommendations for Segments

• Provided tailored recommendations for each segment to enhance customer engagement and retention based on their characteristics and behaviors.

Usage

Dependencies

Ensure you have the following dependencies installed:

• kmeans
• ggplot
• dplyr
• factoextra
• reshape2

Running the Code

• Clone the repository.
• Execute the main script for data processing and segmentation.

Contributions

Contributions are welcome! There are no specific guidelines; any efficient and effective contributions are encouraged to enhance the project.

License

This project is distributed under [choose a license].