Segmentasi Pelanggan Menggunakan Metode K-Means Clustering Berdasarkan Model RFM Pada Klinik Kecantikan (Studi Kasus : Belle Crown Malang)

Savitri, Aulia Dewi (2018) Segmentasi Pelanggan Menggunakan Metode K-Means Clustering Berdasarkan Model RFM Pada Klinik Kecantikan (Studi Kasus : Belle Crown Malang). Sarjana thesis, Universitas Brawijaya.

Abstract

Belle Crown merupakan salah satu klinik kecantikan baru di Malang yang belum menerapkan strategi CRM (Customer Relationship Management) dengan memberikan pelayanan yang berbeda pada setiap pelanggan. Segmentasi merupakan proses untuk mengetahui karakteristik pelanggan dengan kesamaan tertentu, sehingga mempermudah penggalian informasi terkait pelanggan yang profitable. Perilaku bisnis pelanggan dapat dilihat dari Recency {rentang waktu transaksi terakhir), Frequency (jumlah transaksi), dan Monetary (rupiah yang dikeluarkan) atau dikenal dengan analisis RFM (Recency, Frequency, Monetary). Salah satu metode pengelompokan yaitu K-Means yang dapat digunakan untuk melakukan segmentasi tersebut. Hasil grafik dari metode Elbow digunakan untuk membantu dalam menentukan jumlah segmen secara intuitif dalam penerapan metode K-Means. Data yang digunakan dalam penelitian ini adalah riwayat transaksi pelanggan pada periode Mei hingga Oktober 2017 sejumlah 21.513 transaksi dan 4716 pelanggan. Pada penelitian ini menghasilkan dua segmen dan tiga segmen. Analisis berdasarkan nilai RFM menunjukkan bahwa peringkat pertama merupakan kelompok pelanggan yang profitable karena memiliki nilai RFM yang lebih besar dibanding segmen lainnya. Luaran dari penelitian ini adalah visualisasi dashboard dengan beberapa grafik yang memuat hasil segmentasi pelanggan berdasarkan nilai RFM pada layanan-layanan yang diberikan oleh Belle Crown.

English Abstract

Belle Crown is one of new aesthetic centers in Malang has not applied CRM strategy (Customer Relationship Management) by giving different service for all of its costumers. Segmentation is a process undergo to identify costumers with similar characteristics, therefore, it can help to explore more information on profitable costumers. The costumer's business behaviour could be seen from Recency (last transaction range), Frequency (the number of transactions), and Monetary (the amount of money spent) or it is known as RFM (Recency, Frequency, Monetary). One of data clustering method is K-Means that is used to do the segmentation. The graphics result from Elbow method is used to determine the number of segments intuitively during the application of K-Means method. The data used in this research is transaction history taken from May-October 2017 and it includes 21.513 transactions and 4716 costumers. In its application, the research results two kinds of segments including 2 segments and 3 segments. The analysis based on RFM value showed that the first rate is the profitable customer as it has bigger RFM compared to other segments. The superficial of this research is to produce dashboard visualization as the result of costumers segmentation with some graphics based on RFM value of Belle Crown’s service.

Item Type: Thesis (Sarjana)
Identification Number: SKR/FTIK/2018/125/051801112
Uncontrolled Keywords: Segmentasi Pelanggan, Clustering, Analisis RFM, K-Means
Subjects: 000 Computer science, information and general works > 004 Computer science > 004.015 1 Finite mathematic
Divisions: Fakultas Ilmu Komputer > Sistem Informasi
Depositing User: Yusuf Dwi N.
Date Deposited: 03 Jul 2018 01:42
Last Modified: 26 Oct 2021 07:38
URI: http://repository.ub.ac.id/id/eprint/11839
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