SmartCart Intelligent Customer Segmentation System

Project Overview

SmartCart is a growing e-commerce platform serving customers across multiple countries. The company currently uses generic marketing strategies without understanding distinct customer behavior patterns.

This project builds an Intelligent Customer Segmentation System using Unsupervised Machine Learning to discover hidden behavioral patterns and group customers into meaningful clusters.

The objective is to enable:

• Personalized marketing
• High-value customer identification
• Churn-risk detection
• Data-driven business decisions

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Dataset Description

The dataset contains 2240 customer records with 22 features including:

1️⃣ Customer Demographics

• Year_Birth
• Education
• Marital_Status
• Income
• Kidhome
• Teenhome
• Dt_Customer

2️⃣ Purchase Behavior (Spending)

• MntWines
• MntFruits
• MntMeatProducts
• MntFishProducts
• MntSweetProducts
• MntGoldProds

3️⃣ Purchase Behavior (Frequency)

• NumDealsPurchases
• NumWebPurchases
• NumCatalogPurchases
• NumStorePurchases
• NumWebVisitsMonth

4️⃣ Customer Feedback

• Recency
• Complain

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Tech Stack

• Python
• Pandas
• NumPy
• Matplotlib / Seaborn
• Scikit-learn

Clustering algorithms used:

• K-Means
• Hierarchical Clustering
• DBSCAN

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Methodology

1️⃣ Data Preprocessing

• Handled missing values (Income)
• Converted enrollment date into customer tenure
• Feature scaling using StandardScaler
• Removed irrelevant identifiers

2️⃣ Feature Engineering

• Total spending calculation
• Total purchase frequency
• Customer tenure extraction
• Loyalty indicator features

3️⃣ Clustering

• Applied K-Means clustering
• Used Elbow Method to determine optimal clusters
• Evaluated clusters using Silhouette Score
• Visualized clusters using PCA

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Key Insights

The model identified distinct customer segments such as:

• High-Value Premium Customers
• Frequent Discount Shoppers
• High Engagement but Low Purchase Users
• Churn-Prone Customers

These segments can help SmartCart:

• Design targeted campaigns
• Improve retention strategies
• Optimize marketing budget allocation

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Business Impact

By implementing this segmentation system, SmartCart can:

• Increase marketing ROI
• Reduce churn rate
• Improve customer lifetime value (CLV)
• Enable data-driven personalization

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Future Improvements

• Deploy as a web-based dashboard
• Integrate with real-time transaction data
• Apply RFM-based segmentation
• Combine with churn prediction model

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