RFM_Analysis is a side project that aims to perform customer segmentation using the RFM model. This model helps analyze customer behavior and classify customers into different segments based on their buying patterns and engagement with the business.
- RFM stands for Recency, Frequency, and Monetary Value.
- It is a customer segmentation technique widely used in marketing and retail analytics.
- The RFM model analyzes three key aspects of customer behavior:
- Recency: How recently a customer made a purchase or interacted with the business.
- Frequency: How often a customer made purchases or interacted with the business within a specific time period.
- Monetary Value: The total value of purchases made by a customer within a specific time period.
- By combining these three metrics, the RFM model helps identify different segments of customers:
- High-value customers who made a recent purchase and have a high spending frequency.
- Loyal customers who frequently engage with the business, even if their recency or monetary value is lower.
- Churned customers who haven't made a purchase in a long time, indicating a potential loss of interest.
- And more, depending on the specific analysis needs and business objectives.
RFM_Analysis retrieves the data from Kaggle.