# 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
Commented 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 Commented May 23, 2022 at 14:29
• I think @whuber makes a good point. In both cases (total or mean monetary) you would be able to convert to the other case using the frequency. For example with mean monetary we could still capture a "new but heavy spender" because they would have a good recency score, a high mean monetary, and a high frequency. In your original example, if the new customer placed 2 huge orders for 100k and 200k, then their mean monetary value would be 150k, so we know they have made 2 purchases with an average of 150k, so they are a heavy spender! So both mean and total work, just depends on your preference! Commented Apr 29 at 14:06
• I made a pairplot and I think using total is more intuitive for plots and segmentation. But both total and mean make sense. In the case of lifetimes (now replaced by github.com/pymc-labs/pymc-marketing), I think the reason they used certain definitions is because they are not actually doing RFM segmentation analysis, but rather are using RFM as an intermediate for CLV analysis. For example here they have the include_first_transaction variable that they say to change if doing segmentation. Commented Apr 29 at 14:51
• Sorry I realized the last link I gave is not a permalink since it points to main and main can change over time. Here is the permalink Commented May 13 at 11:43