Customer & Seller Segmentation using Machine Learning

An end-to-end data analytics project demonstrating customer and seller segmentation using business analytics, feature engineering, and unsupervised machine learning.

The repository contains two independent segmentation projects built on real-world e-commerce datasets:

• 📊 Customer Segmentation using RFM Analysis
• 🛍️ Seller Segmentation using K-Means Clustering

The goal of these projects is to transform raw transactional data into actionable business insights that support customer relationship management and marketplace optimization.

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Repository Structure

Customer-Seller-Segmentation/
│
├── data/
│
├── notebooks/
│   ├── Customer_RFM_Segmentation.ipynb
│   └── Seller_Segmentation.ipynb
│
├── assets/
│   ├── rfm_segments.png
│   ├── rfm_treemap.png
│   ├── seller_clusters.png
│   ├── seller_heatmap.png
│   └── pca_clusters.png
│
└── README.md

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Project 1 — Customer Segmentation (RFM Analysis)

Objective

Segment customers according to purchasing behavior using the RFM (Recency, Frequency, Monetary) framework to support targeted marketing campaigns and customer retention strategies.

Workflow

• Data Cleaning
• Exploratory Data Analysis
• RFM Feature Engineering
• Customer Scoring
• Segment Assignment
• Customer Profiling
• Business Recommendations
• Data Visualization

Customer Segments

• Champions
• Loyal Customers
• Potential Loyalists
• New Customers
• Promising
• Need Attention
• About to Sleep
• At Risk
• Can't Lose Them
• Hibernating
• Lost

Example Visualizations

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Project 2 — Seller Segmentation

Objective

Identify different seller profiles in an e-commerce marketplace using feature engineering and K-Means clustering to support marketplace management and seller growth strategies.

Workflow

• Data Cleaning
• Exploratory Data Analysis
• Seller-Level Feature Engineering
• Data Preprocessing
• Feature Scaling
• Cluster Evaluation
• K-Means Clustering
• Cluster Profiling
• Business Interpretation
• Business Recommendations

Engineered Features

• Total Products
• Active Product Ratio
• Average Conversion Rate
• Average Product Price
• Average Discount
• Category Diversity
• Premium Product Ratio
• Seller Reliability

Seller Segments

• Enterprise Sellers
• High-Performing Sellers
• Premium Sellers
• Emerging Sellers
• Inactive Sellers

Example Visualizations

Tools & Technologies

Programming

• Python

Data Analysis

• Pandas
• NumPy

Visualization

• Matplotlib
• Seaborn
• Plotly

Machine Learning

• Scikit-learn
• K-Means Clustering
• StandardScaler

Evaluation Metrics

• Silhouette Score
• Davies–Bouldin Index
• Calinski–Harabasz Index

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Business Value

These projects demonstrate how machine learning can be applied to support business decision-making.

Customer Segmentation

• Personalized marketing campaigns
• Customer retention strategies
• Customer lifetime value optimization
• Churn prevention

Seller Segmentation

• Seller performance monitoring
• Marketplace optimization
• Seller onboarding strategies
• Seller retention
• Targeted incentive programs

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Skills Demonstrated

• Data Cleaning
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Data Visualization
• Customer Analytics
• Marketplace Analytics
• Unsupervised Machine Learning
• K-Means Clustering
• Business Analytics
• Business Storytelling

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Future Improvements

• Hierarchical Clustering
• DBSCAN
• Gaussian Mixture Models
• Interactive Dashboard (Plotly / Power BI)
• Automated Segmentation Pipeline
• Model Comparison

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