Customer segmentation is the technique of diving customers into groups based on their purchase patterns to identify who are the most profitable groups. In segmenting customers, various criteria can also be used depending on the market such as geographic, demographic characteristics or behavior bases. This technique assumes that groups with different features require different approaches to marketing and wants to figure out the groups who can boost their profitability the most. Today, we are going to discuss how to do customer segmentation analysis with the online retail dataset from UCI ML repo. This analysis will be focused on two steps getting the RFM values and making clusters with K-means algorithms.

This post is by Gal Oshri, a Program Manager in the Data Group at Microsoft. RFM is a simple and intuitive technique for segmenting customers and has been used by marketers for decades. RFM also has surprising value in machine learning applications despite its simplicity. This blog post describes how a generic technique has allowed us to come within 1% accuracy of winning solutions in various ML competitions, such as placing in the top 30 entries of the KDD Cup 2015 and getting a boost of 502 positions on the leaderboard of an AirBnB Kaggle competition. RFM has been widely used in direct marketing and database marketing for identifying the customers who are most likely to respond or make a purchase [1].