TY - JOUR
T1 - A multi layer recency frequency monetary method for customer priority segmentation in online transaction
AU - Handojo, Andreas
AU - Pujawan, Nyoman
AU - Santosa, Budi
AU - Singgih, Moses Laksono
N1 - Publisher Copyright:
© 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - customer
KW - disruptive
KW - online transaction
KW - recency frequency monetary
KW - segmentation
UR - http://www.scopus.com/inward/record.url?scp=85145817893&partnerID=8YFLogxK
U2 - 10.1080/23311916.2022.2162679
DO - 10.1080/23311916.2022.2162679
M3 - Article
AN - SCOPUS:85145817893
SN - 2331-1916
VL - 10
JO - Cogent Engineering
JF - Cogent Engineering
IS - 1
M1 - 2162679
ER -