A data analysis project using RFM segmentation to identify key customer personas and provide marketing recommendations.
The goal of this project was to analyze a large e-commerce transaction dataset to understand customer behavior. By implementing RFM (Recency, Frequency, Monetary) analysis, the project segments customers into distinct groups to enable targeted and effective marketing strategies.
- Data Manipulation & Analysis: Python (Pandas, NumPy, SQLAlchemy)
- Database: SQL (SQLite)
- Data Visualization: Tableau
- Data Cleaning: Loaded multiple CSV files into a SQL database. Used SQL joins to create a master dataset, which was then cleaned and preprocessed using Python.
- RFM Analysis: Calculated the Recency, Frequency, and Monetary value for each unique customer. These values were then used to create RFM scores.
- Persona Creation: Grouped RFM segments into actionable, named personas such as "Champions," "At-Risk VIPs," and "Newcomers" using rule-based logic.
- Dashboarding: Connected the final, segmented dataset to Tableau to build an interactive dashboard showcasing the findings.
- Identified a key "Champions" segment (customers with high R, F, and M scores) that represents the most valuable customer group.
- Uncovered a significant "At-Risk VIPs" segment, comprising high-value customers who have not made a purchase in a long time, indicating a key opportunity for re-engagement campaigns.
- The final dashboard provides a clear, filterable view of customer distribution, allowing for strategic decision-making.