Comparative Study of Fuzzy C-Means and K-Means Algorithm for Grouping Customer Potential in Brand Limback

Abstract

The customer is a stakeholder for a business, to maintain and increase customer enthusiasm and develop it for the company's performance, it is necessary to do customer segmentation which aims to find out potential customers. This study uses purchase transaction data from Brand Limback customers in the period 2020. The use of RFM (Recency, Frecuency, Monetary) analysis helps in determining the attributes used for customer segmentation. To determine the optimal number of clusters from the RFM dataset, the Elbow method is applied. The datasets generated from RFM are grouped using the Fuzzy C-Means and K-Means algorithms, the two algorithms will compare the quality in the formation of clusters using the Silhoutte Coefficient and Davies-Bouldin Index methods. Customer segmentation from the RFM dataset that has been clustered produces 7 optimal clusters, namely Cluster 0 is a bronze customer. Cluster 1 is a silver customer. Cluster 2 is a gold customer. Cluster 3 is a platinum customer. Cluster 4 is a diamond customer. Cluster 5 is a super customer, and cluster 6 is a superstar customer. The cluster validation of k-means using the silhouette coefficient produces a value of 0.934 while the Davies bouldin index produces a value of 0.155 and the validation results of the fuzzy c-means algorithm using the silhouette coefficient produces a value of 0.921 while the Davies bouldin index produces a value of 0.145.

Analisis Loyalitas Pelanggan Business To Business Berdasarkan Model RFM Menggunakan Algoritma Fuzzy C-Means

“…Ini dilakukan untuk mendapatkan jumla cluster yang tepat dengan tingkat homogenitas terbaik. Untuk mengetaui seberapa baik hasil clustering, dapat dilakukan dengan mengukur validitas cluster melalui salah satu metrik yang umum digunakan yaitu Davies Bouldin Index (DBI) [4]. Nilai DBI yang kecil menunjukkan hasil clustering pada data tersebut terkelompok dengan baik dan semakin homogen [18].…”

“…Dalam pengembangan strategi pemasaran, saat ini berbagai perusahaan tidak hanya mengutamakan strategi berorientasi produk, tetapi juga mengutamakan strategi berorientasi pelanggan [3]. Alasannya adalah karena pelanggan memegang peranan penting dalam pengembangan bisnis dan menjadi sumber keuntungan perusahaan [4]. Salah satu strategi pemasaran berorientasi pelanggan yang efektif dan dapat dilakukan oleh PT.…”

Enhancing Customer Segmentation Insights by using RFM + Discount Proportion Model with Clustering Algorithms

“…One of the most common ways is by using RFM model. RFM will help to understand the behavior and customers value based on their characteristics [7]. Even small businesses can significantly benefit from customer segmentation.…”