RFM (Recency-Frequency-Monetary) analysis is indeed a powerful technique for customer segmentation based on purchasing behavior. Here's a more detailed breakdown of how each component of RFM analysis contributes to understanding customer value:

Recency (R): measures how recently a customer made a purchase. Customers who purchased recently are more likely to purchase again compared to those who haven't purchased in a while. Recency helps identify active and engaged customers.

Frequency (F): measures how often a customer makes a purchase within a given period. Frequent buyers are typically more loyal and likely to return. High frequency indicates strong customer engagement and satisfaction.

Monetary (M): measures how much money a customer has spent over a specific period. High monetary value indicates that the customer contributes significantly to revenue. Understanding which customers spend the most helps in targeting high-value customers for special promotions or loyalty programs.

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Data collected from public sources, which will be processed, checked and cleaned before being used for analysis.

In the analysis the dataset of global retail company was examined to identify RFM segments and find patterns in the customer base. The analysis contains:

• Creating customer segments with RFM analysis.

• Measuring distribution of Recency, Frequency and Monetary

• Analysis of value of RFM segments

• Demographic analysis of RFM segments