Penerapan k-Means Clustering Berdasarkan Analisis RFM Terhadap Segmentasi Pembeli untuk Meningkatkan Strategi CRM

Authors

DOI:

https://doi.org/10.30865/mib.v6i4.4472

Keywords:

CRM, Customer Segmentation, k-Means, RFM, Data Mining

Abstract

An industry requires a good strategy in running its business. Saga Bako is a small industry that sells various types of tobacco and its equipment. However, Saga Bako has not yet implemented a Customer Relationship Management (CRM) strategy in its service to buyers. It is necessary to segment customers to find out less profitable buyers and buyers who provide large profits. The use of data mining also contributes when segmenting customers through the use of purchase data. The methodology applied in this research is CRISP-DM with purchase data at Saga Bako from January to March 2022. The k-means algorithm is applied in the formation of clusters based on the Recency, Frequency, Monetary (RFM) model, with the help of Weka 3.8.5 tools. The Elbow method is used to determine the best number of clusters (k). The results obtained are from 47 buyers with 663 transaction data divided into three clusters, 26 low potential buyers, ten medium potential buyers, and 11 high potential buyers.

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Published

2022-10-25

Issue

Vol. 6 No. 4 (2022): Oktober 2022

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