📊 Customer Segmentation using RFM and K-Means Clustering

🔍 Problem Statement

Understanding customer behavior is crucial for businesses. By segmenting customers based on their purchasing patterns, businesses can:

• Identify high-value customers.
• Recognize at-risk customers.
• Personalize marketing strategies for customer retention and acquisition.

📌 Dataset

The dataset consists of transaction records from an e-commerce platform with the following key attributes:

• InvoiceNo – Unique invoice identifier.
• StockCode – Product identifier.
• Description – Product description.
• Quantity – Number of units purchased.
• InvoiceDate – Date of purchase.
• UnitPrice – Price per unit.
• CustomerID – Unique customer identifier.
• Country – Country of purchase.

🛠️ Methodology

1️⃣ Data Preprocessing

• Handled missing values and duplicates.
• Converted InvoiceDate to a datetime format.
• Removed outliers using Z-score analysis.

2️⃣ RFM Analysis

• Recency (R): Days since the customer’s last purchase.
• Frequency (F): Number of transactions by each customer.
• Monetary Value (M): Total spend per customer.

3️⃣ K-Means Clustering

• Determined the optimal number of clusters using the Elbow Method and Silhouette Score.
• Segmented customers into 4 clusters.

4️⃣ Insights from Clusters

• Cluster 3: Loyal and high-spending customers.
• Cluster 2: Less engaged customers with high recency.
• Cluster 1 & 0: Intermediate customers who can be nurtured.

📈 Results & Visualizations

• Pair plots to analyze feature distributions across clusters.
• Box plots to compare Recency, Frequency, and Monetary Value across clusters.
• Bar charts showing average RFM values per cluster.

🔧 Technologies Used

• Python
• Pandas, NumPy – Data Manipulation
• Matplotlib, Seaborn – Data Visualization
• Scikit-Learn – Machine Learning (K-Means Clustering)
• StandardScaler – Feature Scaling

📌 Future Enhancements

• Implement Hierarchical Clustering for better interpretability.
• Develop automated customer insights with dashboards.
• Integrate with real-time e-commerce data.