A multi layer recency frequency monetary method for customer priority segmentation in online transaction

Customer segmentation is a critical step toward appropriately differentiating
services to different customers. One common way of segmenting customers
is by using what is called Recency, Frequency, Monetary (RFM) approach, where
customers are classified based on the recency of their transactions as well as how
often they purchase goods and services and how much money they spent. However,
this approach is not able to fairly differentiate customers especially when it comes
to the cases where old customers have decreased or stopped their purchases and
the new customers just started buying. In order to overcome this, we proposed
what is called Multi Layer Recency, Frequency, and Monetary (MLRFM) approach. In
this approach, we divide time periods into multiple layers and the recency, frequency,
and monetary values are analyzed considering these different segments.
Our numerical examples show that this multi layer approach can provide a good
alternative for the companies that sell products online and customers are behaving
very dynamically.