🛒 E-Commerce Cancellation & Customer Segmentation Analysis

📌 Project Overview

This project analyzes customer behavior and cancellation patterns using the Kaggle E-Commerce dataset.
The goal is to identify key drivers of cancellations, quantify revenue impact, and segment high-risk customers to support data-driven business decisions.

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🎯 Objectives

• Analyze overall cancellation rate
• Quantify revenue lost due to cancellations
• Identify high-risk products and customer segments
• Explore customer behavior patterns
• Build actionable business insights from transactional data

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📊 Key Metrics Analyzed

• Overall Cancellation Rate
• Revenue Lost from Cancellations
• Top Products by Cancellation Count
• Top Products by Revenue Impact
• Customer-Level Cancellation Rate
• Segment-Based Cancellation Trends

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📈 Sample Insights

• A small subset of products drives a disproportionate share of cancellation impact.
• Certain customer segments exhibit significantly higher cancellation rates.
• Revenue-weighted cancellation analysis provides stronger business insight than raw counts.
• High-value customers with frequent cancellations may require targeted retention strategies.

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🛠️ Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-Learn (for segmentation & modeling)

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📂 Dataset

Source: Kaggle – E-Commerce Data

The dataset contains transactional-level order data including:

• Customer ID
• Product / Stock Code
• Quantity
• Price
• Invoice Information
• Cancellation Indicators

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🔎 Analysis Workflow

1. Data Cleaning & Preprocessing
2. Feature Engineering (Total Price, Cancellation Flags)
3. Exploratory Data Analysis (EDA)
4. Cancellation Rate & Revenue Impact Analysis
5. Customer-Level Behavioral Segmentation
6. Visualization & Business Interpretation

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💡 Business Value

This analysis helps answer:

• Which products contribute most to cancellation revenue loss?
• Which customer segments are high-risk?
• What is the financial impact of cancellations?
• Where should retention strategies be prioritized?

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📊 Example Visualization

• Confusion Matrix (if predictive model applied)
• Revenue Impact Bar Charts
• Cancellation Distribution Charts
• Segment-Level Risk Heatmaps

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🚀 Future Improvements

• Build predictive cancellation model
• Revenue-weighted churn scoring
• Time-series cancellation forecasting
• Customer lifetime value analysis
• Risk-based intervention strategies

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