Explorations of RFM and k-means techniques to segment customer data

RFM allows segmentation of customer purchasing history according to dimensions of recency of purchase, frequency of purchase, and monetary value of purchase. The resulting segments can be approached thereafter with different engagement options. The K-means analysis with wine purchases is using response data to cluster similar purchasing groups. There are also some summary visuals here using seaborn. Thanks to @susanli2016 and @glamp for the walkthroughs and starting code that kicked off the exploration here.

• https://towardsdatascience.com/find-your-best-customers-with-customer-segmentation-in-python-61d602f9eee6
• http://blog.yhat.com/posts/customer-segmentation-using-python.html
• https://archive.ics.uci.edu/ml/datasets/Online+Retail