TELKOMNIKA Telecommunication Computing Electronics and Control
Customer segmentation with RFM models and demographic variable using DBSCAN algorithm
Dublin Core
Title
TELKOMNIKA Telecommunication Computing Electronics and Control
Customer segmentation with RFM models and demographic variable using DBSCAN algorithm
Customer segmentation with RFM models and demographic variable using DBSCAN algorithm
Subject
Customer segmentation
DBSCAN
Demographic
RFM
Silhouette index
DBSCAN
Demographic
RFM
Silhouette index
Description
The aims of this research was to identify prospective customers by
conducting customer segmentation based on recency, frequency, monetary
(RFM) values and demographic variables. The step were selected the data and
normalized. The normalized data were clustered using the density based spatial
clustering of applications with noise (DBSCAN) algorithm. The k-dist graph
was utilized with RStudio tools to identify the best values for epsilon and
MinPts. The outcome of utilizing epsilon 0.06 and MinPts 3 was the
identification of 5 clusters and 31 data points considered as noise, resulting in a
silhouette index (SI) value of 0.4222. Based on the average RFM values,
cluster 1 was categorized as prospective customers, while clusters 2, 3, 4,
and 5 were designated as loyal customers. Furthermore, according to
demographic analysis, the majority of customers are between the ages of 35
to 45, female, married, and housewives. Women, groceries, such as rice and
cooking oil, were the most popular products. Besides, the customers were
mostly lecturers and lived in Pekanbaru. This was compatible with the
customer target of people from upper middle class, such as lecturers, and
with the location of the mart as well, which was near a campus.
conducting customer segmentation based on recency, frequency, monetary
(RFM) values and demographic variables. The step were selected the data and
normalized. The normalized data were clustered using the density based spatial
clustering of applications with noise (DBSCAN) algorithm. The k-dist graph
was utilized with RStudio tools to identify the best values for epsilon and
MinPts. The outcome of utilizing epsilon 0.06 and MinPts 3 was the
identification of 5 clusters and 31 data points considered as noise, resulting in a
silhouette index (SI) value of 0.4222. Based on the average RFM values,
cluster 1 was categorized as prospective customers, while clusters 2, 3, 4,
and 5 were designated as loyal customers. Furthermore, according to
demographic analysis, the majority of customers are between the ages of 35
to 45, female, married, and housewives. Women, groceries, such as rice and
cooking oil, were the most popular products. Besides, the customers were
mostly lecturers and lived in Pekanbaru. This was compatible with the
customer target of people from upper middle class, such as lecturers, and
with the location of the mart as well, which was near a campus.
Creator
Siti Monalisa, Yosie Juniarti, Eki Saputra, Fitriani Muttakin, Tengku Khairil Ahsyar
Source
http://telkomnika.uad.ac.id
Date
Oct 26, 2022
Contributor
PERI IRAWAN
Format
PDF
Language
ENGLISH
Type
TEXT
Files
Collection
Citation
Siti Monalisa, Yosie Juniarti, Eki Saputra, Fitriani Muttakin, Tengku Khairil Ahsyar, “TELKOMNIKA Telecommunication Computing Electronics and Control
Customer segmentation with RFM models and demographic variable using DBSCAN algorithm,” Repository Horizon University Indonesia, accessed March 12, 2025, https://repository.horizon.ac.id/items/show/4562.
Customer segmentation with RFM models and demographic variable using DBSCAN algorithm,” Repository Horizon University Indonesia, accessed March 12, 2025, https://repository.horizon.ac.id/items/show/4562.