This repo contains the Customer Segmentation project as part of my Data Science portfolio. The project documents the steps to implement customer segmentation in Python 3 code. The segmentation technique used in this project is known as RFM (Recency, Frequency, Monetary) analysis. RFM analysis is a behaviour-based approach of grouping customers into segments or clusters, based on their previous purchase transactions i.e. how recently, how often, and how much did a customer buy.
As an e-commerce platform, it is very important to profile your customers, dividing your clientele base into groups based on their needs and expectations. Grouping will help us come up with dedicated marketing strategies and will aid us in recommending products to different user bases. In this project, we are interested in analyzing the content of an E-commerce database that lists purchases made by ∼4000 customers over a period of one year (1/12/2010 to 9/12/2011). Based on this analysis, we would like to develop models to group the 4000 customers into different buckets. Such a model must take into account the similarity between the products purchased between the users (i.e. a user might purchase 2 different products which are very similar to each other), the spending patterns of a user, their meta information, etc.
https://drive.google.com/file/d/1jLeQLgChgCaHAz0yFXKjztudp1pfwJf8/view
RFM (Recency, frequency, monetary value)