This project involves segmenting bank customers based on their transaction data using RFM (Recency, Frequency, Monetary) analysis and K-means clustering. The goal is to identify distinct customer segments to tailor marketing strategies and improve customer retention.
This dataset contains over a million entries, detailing customer demographics, account balances, and transaction information, with variables including customer ID, date of birth, gender, location, and transaction specifics such as date, time, and amount.
RFM (Recency, Frequency, Monetary) analysis is a method used to quantify customer value. It is based on three dimensions:
- Recency (R): How recently a customer made a purchase.
- Frequency (F): How often a customer makes a purchase.
- Monetary (M): How much money a customer spends. By scoring customers on these three dimensions, businesses can identify their most valuable customers and develop targeted marketing strategies.
Formula used for RFM Calculation
0.15 * Recency Score + 0.28 * Frequency Score + 0.57 * Monetary Score
| RFM Score Range | Segment |
|---|---|
| RFM > 4 | Platinum |
| 4 > RFM >= 3 | Gold |
| 3 > RFM >= 2 | Silver |
| RFM < 2 | Bronze |
K-means is a popular clustering algorithm used to partition data into distinct groups (clusters). The algorithm aims to minimize the within-cluster variance by iteratively assigning data points to the nearest cluster centroid and then recalculating the centroids based on the current assignments.
In this project, we applied K-means clustering to the RFM scores to identify distinct customer segments.
This project demonstrates the application of RFM analysis and K-means clustering for customer segmentation in the banking sector. By understanding the behavior and value of different customer segments, banks can develop more effective marketing strategies and enhance customer loyalty.
pyenv local 3.11.3
python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip