📊 E-commerce Analytics Project

🔍 Project Overview

This project explores an online retail dataset to analyze revenue trends, customer behavior, and retention patterns. The goal was to simulate a real-world business analysis workflow — from raw data cleaning to building an interactive dashboard.

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🎯 Objectives

• Analyze revenue trends over time
• Identify top-performing products
• Identify high-value customers
• Segment customers using RFM analysis
• Evaluate customer retention using cohort analysis

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📈 Key Insights

• Revenue shows clear seasonality, peaking in November (~1.15M), indicating strong Q4 performance
• The top 10 customers generate approximately 17% of total revenue, highlighting high customer concentration
• A small number of products dominate sales, with the top product generating over 160K in revenue
• Customer spending is highly skewed: the average revenue per customer (~2048) is significantly higher than the median (~668), indicating the presence of high-value clients
• Cohort analysis shows that over 70% of customers do not return after their first purchase, suggesting weak customer retention

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🛠 Tools & Technologies

• Python (Pandas)
• Jupyter Notebook
• Tableau Public

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📊 Dashboard

👉 View Tableau Dashboard

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📁 Data

Due to file size limitations, the cleaned dataset is not included in this repository.

You can access the dataset here: 👉 Download Data

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⚙️ Data Cleaning Steps

• Removed transactions with negative or zero quantity
• Removed transactions with zero unit price
• Dropped missing Customer IDs
• Created a new feature: Revenue = Quantity × UnitPrice
• Converted InvoiceDate to datetime format
• Removed duplicate records
• Filtered out non-product entries (e.g., POSTAGE, Manual)

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📊 Analysis Performed

• Revenue over time
• Top 10 products by revenue
• Top 10 customers by revenue
• Customer segmentation (RFM)
• Cohort retention analysis

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🚀 What I Learned

• How to clean and preprocess real-world transactional data
• How to extract meaningful business insights
• How to perform customer segmentation (RFM)
• How to build and present dashboards in Tableau
• How to structure an end-to-end analytics project

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Next Steps

• Add more advanced customer segmentation
• Build predictive models (e.g., customer lifetime value)
• Improve dashboard interactivity