Comparison of the RFM Model's Actual Value and Score Value for Clustering
Dublin Core
Title
Comparison of the RFM Model's Actual Value and Score Value for Clustering
Subject
RFM model; RFM actual value; RFM core value; clustering
Description
Clustering algorithms and Recency-Frequency-Monetery (RFM) models are widely implemented in various sectors of ecommerce, banking, telecommunications, and other industries to obtain customer segmentation. The RFM model will assess a
line of data which includes the recency and frequency of data appearance as well as the monetary value of a transaction made
by a customer. Choosing the right RFM model also influences the analysis of cluster results, the output of cluster results is
more compact for the same clusters (inter-cluster) and separate for other clusters (intra-cluster). Through an experimental
approach, this research aims to find the best dataset transformation model between actual RFM values and RFM scores. The
method used is to compare the actual RFM value model and the RFM score and use the silhouette score value as an indicator
to get the best clustering results using the K-Means algorithm. The subject of this research is a stall-based e-commerce
application, where data was taken in the Wiradesa area, Central Java. The resulting dataset consisted of 273,454 rows with
18 attributes from January 2022 to December 2022 through collecting historical data from shopping outlets to wholesalers.
Analysis of the dataset was carried out by transforming the dataset using the RFM method into actual values and score values,
then the dataset was used to obtain the best cluster data. The results of this research show that transaction data based on time
(time series) can be transformed into data in the RFM model where the RFM model's actual value is better than the RFM score
model with a silhouette score = 0.624646 and the number of clusters (K) =3. The results of the clustering process also form a
series of data with a cluster label, thus forming supervised learning data.
line of data which includes the recency and frequency of data appearance as well as the monetary value of a transaction made
by a customer. Choosing the right RFM model also influences the analysis of cluster results, the output of cluster results is
more compact for the same clusters (inter-cluster) and separate for other clusters (intra-cluster). Through an experimental
approach, this research aims to find the best dataset transformation model between actual RFM values and RFM scores. The
method used is to compare the actual RFM value model and the RFM score and use the silhouette score value as an indicator
to get the best clustering results using the K-Means algorithm. The subject of this research is a stall-based e-commerce
application, where data was taken in the Wiradesa area, Central Java. The resulting dataset consisted of 273,454 rows with
18 attributes from January 2022 to December 2022 through collecting historical data from shopping outlets to wholesalers.
Analysis of the dataset was carried out by transforming the dataset using the RFM method into actual values and score values,
then the dataset was used to obtain the best cluster data. The results of this research show that transaction data based on time
(time series) can be transformed into data in the RFM model where the RFM model's actual value is better than the RFM score
model with a silhouette score = 0.624646 and the number of clusters (K) =3. The results of the clustering process also form a
series of data with a cluster label, thus forming supervised learning data.
Creator
Samidi, Ronal Yulyanto Suladi, Dewi Kusumaningsih
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
December 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Samidi, Ronal Yulyanto Suladi, Dewi Kusumaningsih, “Comparison of the RFM Model's Actual Value and Score Value for Clustering,” Repository Horizon University Indonesia, accessed January 12, 2026, https://repository.horizon.ac.id/items/show/10130.