This project focuses on customer segmentation using the RFM (Recency, Frequency, Monetary) analysis method. Customer segmentation is a vital strategy for businesses to understand their customers better and tailor marketing and service efforts accordingly.
- Recency (R): Measures how recently a customer made a purchase.
- Frequency (F): Quantifies how often a customer buys.
- Monetary (M): Reflects how much money a customer spends.
The RFM analysis is a powerful technique to classify customers into meaningful segments, such as:
- High-Value Customers: High R, F, and M.
- Loyal Customers: High F and M, moderate R.
- Churning Customers: Low F and M, high R.
This project includes data preprocessing, RFM calculation, customer segmentation, and visualization to facilitate marketing strategies.
- Data Preparation: Cleaning and formatting customer purchase data.
- RFM Calculation: Compute Recency, Frequency, and Monetary scores.
- Customer Segmentation: Apply RFM to group customers into segments.
- Visualization: Generate visual representations of customer segments.
- Clone this repository to your local machine.
- Install the required dependencies (e.g., Python libraries).
- Upload your customer purchase data.
- Execute the RFM analysis scripts.
- Explore the resulting customer segments and actionable insights.
python rfm_analysis.py