Customer Market Analysis
- Kwnstantinos Lambrou
- Dec 13, 2024
- 1 min read
Updated: Jan 21
Customer Segmentation and Pareto Analysis with RFM
Project Overview
In this project, I utilized the RFM (Recency, Frequency, Monetary) model to analyze customer purchasing behavior. By applying the Pareto Principle (80/20 Rule), I identified the most valuable customers contributing significantly to total sales. This analysis serves as a cornerstone for strategic marketing and customer retention efforts.
Project Highlights
RFM Scoring: Segmented customers dynamically using quantile-based scoring to identify VIPs, loyal customers, and at-risk groups.
Pareto Validation: Verified the Pareto Principle by demonstrating that 23% of customers generate 74% of total sales.
Dynamic Visualizations: Developed engaging visual insights using Python libraries like matplotlib and seaborn.
Key Takeaways
Understanding customer behavior helps businesses tailor strategies for retention and growth.
The Pareto Principle remains a powerful tool for identifying high-value customers.
Data-driven segmentation allows for precision in marketing and loyalty program design.
Acknowledgment
This project was part of my Data Science Bootcamp at Big Blue Data Academy, where I mastered the tools and techniques needed to excel in advanced analytics.
View the Code
To see the full code for this project and possibly contribute or fork the repository for your use, please visit my GitHub repository.
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