Revealing Shopping Personalities: Customer Segmentation

Project Overview

This project applies K-Means Clustering to segment mall customers based on their annual income and spending score. By identifying distinct customer groups, businesses can tailor marketing strategies, enhance customer experiences, and improve retention.

The model groups customers into five unique clusters, each representing different spending behaviors. A Streamlit web application was developed to allow users to interact with the segmentation results visually.

Key Insights

• Customer segmentation helps businesses understand diverse shopping behaviors and personalize engagement.
• The K-Means algorithm effectively groups customers based on similarities in income and spending.
• Five clusters were identified, each representing different spending personalities:
  • Mid Income, Mid Spending: Balanced spenders.
  • High Income, Low Spending: Conservative spenders.
  • Low Income, Low Spending: Budget-conscious shoppers.
  • Low Income, High Spending: Impulse buyers.
  • High Income, High Spending: Luxury shoppers.
• Businesses can use these insights to develop targeted marketing campaigns and improve customer satisfaction.

Technical Implementation

Technical Implementation Comes Here

• Data Preprocessing: Scaled numerical features using StandardScaler for better clustering performance.
• Modeling Approach: Applied K-Means Clustering with the Elbow Method to determine the optimal number of clusters.
• Cluster Visualization: Used Matplotlib and Seaborn to plot customer segments.
• Interpretation: Analyzed clusters to understand the shopping behavior of different customer groups.
• Deployment: Developed an interactive Streamlit web app for users to explore the clusters dynamically.

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Key Learnings

• K-Means Clustering effectively groups customers with similar shopping behaviors.
• Feature scaling is crucial for improving clustering performance.
• Visualizing clusters helps in better understanding customer segmentation results.
• Deploying with Streamlit makes the project more interactive and user-friendly.
• Businesses can leverage customer segmentation to enhance marketing strategies and boost sales.