# RFM Customer segmentation - Why Avg monetary value instead of total monetary value?

I am trying to segment our customers based on their purchase data. And I came to know about the RFM technique (Recency, Frequency and Monetary) through these posts here, here etc.

Recency - How recently they made a purchase

Frequency - How many times they made a purchase

Monetary - How much revenue did company got from that customer

I also came across a python package called lifetimes here

while I understand the idea of RFM, I am confused as to why do the consider average revenue of a customer (from all his/her transactions) instead of Total revenue for all his/her transactions?

For ex: If a new customer places 2 huge orders for 100K and 200K, then he contributed 300K to the company and could be classified as "New but promising" or "New but Heavy spender" etc.

But doesn't taking average normalize everyone on the equal scale? So, then monetary value doesn't become useful metric to segment customers. Instead we have to use only Recency and Frequency (because they have raw values).

Is there any reason why you think average revenue is better than Total revenue?

• Given you have frequency data, don't total and mean revenue provide equivalent information?
– whuber
May 23, 2022 at 14:02
• Meaning, why are customers scored on their mean revenue to the company? Basically, in RFM, they score customers based on their values of recency, frequency and monetary. So, if I take average revenue to score a customer, then it is very much possible that I miss niche segments like "New but heavy spenders"...so, I am trying to understand what could be the idea behing scording customers based on average May 23, 2022 at 14:29