Customer Segmentation Using RFM Analysis

Project Overview

This project involves analyzing transactional data from a UK-based online retail business, covering all transactions occurring between 01/12/2010 and 09/12/2011.

The primary objective is to segment customers using the Recency, Frequency, and Monetary (RFM) model, a popular framework for understanding customer behavior and enhancing marketing strategies through personalized efforts.

Dataset

The dataset contains key information, including:

• InvoiceNo: Unique identifier for each transaction.
• StockCode: Product identifier.
• Description: Product details.
• Quantity: Number of products sold per transaction.
• InvoiceDate: Date and time of each transaction.
• UnitPrice: Price per unit of product.
• CustomerID: Unique customer identifier.
• Country: Customer's country of residence.

Approach

1. Data Understanding

• Explored the dataset to comprehend its structure and identify key variables.
• Reviewed statistical summaries to assess the dataset's quality and completeness.

2. Data Cleaning

• Handled missing values and duplicate entries.
• Removed invalid or irrelevant transactions, such as cancellations.
• Standardized data types for consistency.

3. Data Preparation

• Computed additional metrics, such as the TotalPrice (Quantity × UnitPrice).
• Extracted recency, frequency, and monetary values for each customer.

4. Customer Segmentation Modeling

• Applied the RFM framework:
  • Recency: Days since the last purchase.
  • Frequency: Number of transactions.
  • Monetary: Total spending.
• Used K-Means Clustering to segment customers into distinct groups based on their RFM scores.
• Evaluated the optimal number of clusters using methods like the Elbow Curve and Silhouette Analysis.

5. Insights and Recommendations

• Interpreted each customer cluster based on its behavior patterns.
• Provided actionable strategies tailored to each segment to improve engagement, retention, and revenue.

Insights

• Identified distinct customer segments, such as high-value loyal customers, cost-conscious frequent shoppers, and dormant customers.
• Highlighted patterns in customer purchasing behavior and their impact on overall business performance.

Recommendations

• Retain loyal customers through premium loyalty programs, personalized offers, and exclusive deals.
• Re-engage dormant customers with targeted win-back campaigns and discounts.
• Upsell and cross-sell products to frequent shoppers to enhance their spending potential.
• Focus on improving customer lifetime value (CLV) by transitioning lower-value segments into higher-value clusters.

Tools and Technologies

• Programming Languages: Python
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
• Clustering Algorithms: K-Means
• Evaluation Metrics: Silhouette Score, Elbow Method