Analisis Segmentasi Pelanggan Menggunakan Kombinasi RFM Model Dan Teknik Clustering

Abstract

Intense competition in the business field motivates a small and medium enterprises (SMEs) to manage customer services to the maximal. Improve of customer royalty by grouping cunstomers into some of groups and determining appropriate and effective marketing strategies for each group. Customer segmentation can be performed by data mining approach with clustering method. The main purpose of this paper is customer segmentation and measure their loyalty to a SME’s product. Using CRISP-DM method which consist of six phases, namely business understanding, data understanding, data preparatuin, modeling, evaluation and deployment. The K-Means algorithm is used for cluster formation and RapidMiner as a tool used to evaluate the result of clusters. Cluster formation is based on RFM (recency, frequency, monetary) analysis. Davies Bouldin Index (DBI) is used to find the optimal number of clusters (k). The customers are divided into 3 clusters, total of customer in first cluster is 30 customers who entered in typical customer category, the second cluster there are 8 customer whho entered in superstar customer and 89 customers in third cluster is dormant cluster category.

Perbandingan Metode Fuzzy C-Means Dan K-Means Dalam Klasifikasi Kelulusan Mahasiswa (Studi Kasus : Jurusan Manajemen, Universitas Maritim Raja Ali Haji

“…Normalisasi data sangat diperlukan sebelum proses data mining supaya tidak ada parameter yang mendominasi dalam perhitungan jarak antar data. Normalisasi data yang digunakan yaitu normalisasi skor z (zero-mean normalization) yang merupakan salah satu metode atau teknik yang diterapkan untuk normalisasi data, dimana nilai dari atribut X dinormalisasikan berdasarkan rata-rata dan standar deviasi dari atribut X dengan rumus [3]:…”

Penentuan Strategi Promosi Usaha Mikro Kecil Dan Menengah (UMKM) Menggunakan Metode CRISP-DM dengan Algoritma K-Means Clustering

“…Hasil dari penelitian ini terbentuk 3 kelompok pelanggan. Kelompok pertama berjumlah 30 pelanggan yang masuk kedalam kategori typical customer, 8 pelanggan masuk dalam kategori superstar, dan 89 pelanggan masuk pada kategori dormant customer [10].…”

Balance as One of the Attributes in the Customer Segmentation Analysis Method: Systematic Literature Review

“…Thus, the companies are able to set target rightly and customers are going to be more interested and satisfied. Presently, the companies should not only focus on the companies' strategy prioritizing products, but also need to focus on the companies' strategy prioritizing customers (customer oriented) [1].…”

“…Customer segmentation is demanded to classify customers who have similar characteristics to find out consumer behavior. This helps in controlling the right marketing strategy to expediently improve company revenue [1]. Numerous methods for segmenting customers are many.…”