This project involves RFM analysis and developing customer segments based on their RFM scores. The analysis provides insights into customer behaviour and identification of high-value customers, at-risk customers, and potential opportunities for personalized marketing campaigns.
The main steps followed in the analysis involved:
- Uploading the dataset using the required libraries.
- Understand the Dataset: Recency (R): The last time a customer made a purchase. Frequency (F): How often a customer makes a purchase within a specific period. Monetary (M): The total amount of money a customer has spent.
- Data Preprocessing: Clean the Data: Handle missing values, outliers, and inconsistencies in the dataset. Calculate RFM Values: Calculate R, F, and M values for each customer based on the transaction data. -RFM Calculation: Recency (R): Calculate the number of days since the customer's last purchase. Frequency (F): Count the number of purchases made by each customer. Monetary (M): Calculate the total amount spent by each customer.
- RFM Score Calculation: Quartile Analysis: Divide R, F, and M values into quartiles to assign scores from 1 to 4. RFM Score: Combine the individual R, F, and M scores to create a 3-digit RFM score for each customer. -Customer Segmentation: Segment Definition: Define segments based on RFM scores ( High-Value, At-Risk, Low-Value). Segment Interpretation: Understand the characteristics and behavior of each segment.
- Insights and Opportunities: High-Value Customers: Identify customers with high R, F, and M scores. They are your most valuable customers. At-Risk Customers: Look for customers with a high recency score (e.g., 1) but lower frequency and monetary scores. These customers are at risk of churn. Low-Value Customers: Identify customers with low R, F, and M scores. They might be occasional buyers with low spending potential. Opportunities: Explore segments with high frequency but low monetary value. These customers might respond well to upselling or cross-selling strategies.