E-Commerce Data Analysis

Project Overview

This project analyzes e-commerce data from an online retail entity, spanning from December 2010 to December 2011. The analysis covers customer behavior, product performance, geographical insights, and time-based trends. The focus is on improving customer segmentation using RFM analysis, optimizing product offerings, and understanding customer engagement to drive strategic marketing decisions.

Key Components

• Customer Analysis: Segmenting customers using RFM (Recency, Frequency, Monetary) analysis and profiling each segment for targeted marketing strategies.
• Product Analysis: Identifying top-selling products, return rates, and overall revenue contributions.
• Time Analysis: Exploring order trends across different days, months, and seasons, identifying peak shopping periods.
• Geographical Analysis: Understanding purchase behavior across different regions, with a focus on the UK market.
• Returns and Refunds: Analyzing return rates and identifying problematic product categories.
• Revenue Analysis: Assessing total revenue contributions from key products.

Data Preprocessing

The dataset, sourced from the UCI Machine Learning Repository, was cleaned to handle missing values, cancellations, and outliers. This involved converting data types and removing invalid entries, ensuring accurate and actionable insights.

Methodology

• RFM Analysis: Segments customers into four clusters using K-Means clustering to understand customer engagement and value.
• K-Means Clustering: Determined the optimal number of clusters using the elbow method.
• Outlier Removal: Applied the Interquartile Range (IQR) technique to remove outliers from the dataset.

Insights and Recommendations

1. Customer Segmentation: Four distinct segments were identified: Engaged Regulars, Infrequent Shoppers, Loyal High Spenders, and One-Time High Rollers. Tailored marketing strategies were recommended for each.
2. Product Optimization: Top-selling products like 'Party Bunting' contribute significantly to revenue, while some categories show high return rates, requiring further review.
3. Geographical Trends: The UK is the dominant market, but there are opportunities for expansion in other regions.

How to Use

1. Clone the repository:

   git clone https://github.com/YOUR_USERNAME/e-commerce-data-analysis.git