RFM (Recency, Frequency, Monetary) analysis is a proven marketing model for behavior based customer segmentation. It groups customers based on their transaction history – how recently, how often and how much did they buy. RFM helps divide customers into various categories or clusters to identify customers who are more likely to respond to promotions and also for future personalization services.

Given that you have stored this information into your database (i.e. CRM system), you can then divide your customers into various categories or clusters to identify those who are more likely to respond to specific offers/campaigns as well as for future personalization services. Common practice when it comes to segmentation is to think that ‘big spenders’ are the most valuable clients. But what if they purchased only once or a very long time ago? Do they still use our product? It makes sense to reward all of our clients that keep buying our services/products on a regular basis, spending as much money as possible. Are you able to track that? The answer is positive.

Doing this analysis at scale & fast. Although a lot of people still use Excel, you can use the Python code stated here to get your results in just a few seconds.