EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas

RFM (Recency, Frequency, Monetary) analysis provides a method for behavioral customer segmentation beyond simple revenue metrics. The process involves preparing sales data in Pandas by filtering for known customers and aggregating their transactions to calculate individual R, F, and M values. These raw numbers are then converted into ranked scores, typically from 1 to 5, using quantile-based binning to compare customers against each other. This scoring system facilitates the creation of meaningful segments like "Champions" or "At-Risk" customers. Ultimately, this allows businesses to implement more targeted and effective marketing strategies.