Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal CoreIT
Zebua, Rina Sisca
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Determination of Discounts Using K-Means Clustering with RFM Models in Retail Business Zebua, Rina Sisca; Heroza, Rahmat Izwan; Adrian, Monterico; Atrinawati, Lovinta Happy
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 1 (2022): June 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (835.8 KB) | DOI: 10.24014/coreit.v8i1.14695

Abstract

Intense competition in the business sector motivates every company to manage services to regular consumers to the fullest. Increase customer loyalty can be done by grouping customers into several groups and determine appropriate and effective marketing strategies for each group. This study aims to propose the right targeting of discounts that can increase customer loyalty in the retail business. Customer grouping uses data mining techniques with the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, which is divided into six phases, namely business understanding, data understanding, data preparation, modelling, evaluation, and deployment. The formation of this cluster uses k-means clustering method and is based on RFM (recency, frequency, monetary) analysis. From the results of the silhouette test on 2734 transaction data from 210 customers of PT. XYZ from October 2019 to March 2020, three customer clusters were formed. From these three clusters, one cluster that has the best frequency and monetary values is chosen so that it is considered the worthiest group to be given a discount in order to maintain its loyalty.
Co-Authors Atrinawati, Lovinta Happy Monterico Adrian Rahmat Izwan Heroza