RFM (Recency, Frequency, Monetary) Analysis on an Online Retail Customers using K-Means Clustering with Python

The Online Retail data contains customer purchasing history. Including CustomerID, Stock Code, Item Description, Quantity, Unit Price, Invoice Date, and Country.

Dataset : https://www.kaggle.com/vijayuv/onlineretail

Business Understanding

PROBLEM : Only have basic customer data but want to know customer behaviour to plan marketing strategy accordingly.

QUESTION : How to understand customer behaviour to plan marketing strategy accordingly?

MEASURE : A well clustered customer basen on basic data that we have.

RFM Analysis for Customer Segmentation

RFM analysis is a data-driven customer behavior segmentation methodology in which RFM stands for recency, frequency, and monetary value. The concept is to segment customers based on when their last purchase was (Recency), how many times they have purchased in the past (Frequency), and how much they have spent (Monetary).

RFM analysis enables personalized marketing, improves satisfaction and helps marketers to develop unique, appropriate deals for the right customer groups.

Conclusion

Most customers are rookie customers and regular customers. There are very few loyal and core customers in this online retail. This may be because the online retail store has not been around for a long time and is still progressing towards success.

References

• “RFM Analysis: An Effective Customer Segmentation technique using Python” by Anand Singh

https://link.medium.com/oyaAaPRtRab

• “Customer Segmentation with RFM Analysis & Kmeans Clustering” by Anugrah Nurhamid

https://link.medium.com/lnqM7AVtRab

• “Find Your Best Customers with Customer Segmentation in Python” by Susan Li

https://link.medium.com/0GA3V7StRab

• Pareto Chart

https://tylermarrs.com/posts/pareto-plot-with-matplotlib/