The first objective was to categorize the consumer base into appropriate customer segments. The second was to predict the purchases for the current year and the next year based on the customers' first purchase. Given a dataset of transactions (Online Retail dataset from UCI Machine Learning repository) the segments are obtained, common patterns found and grouped into categories that can be later used by the marketer or retailer.
The goal of RFM Analysis is to segment customers based on buying behavior. The historical actions of individual customers for each RFM factor need to be understood. Then, the customers are ranked based on each individual RFM factor, and finally pull all the factors together to create RFM segments for targeted marketing.
● RECENCY (R): Days since last purchase
● FREQUENCY (F): Total number of purchases
● MONETARY VALUE (M): Total money this customer spent.
