Penerapan Algoritma Fuzzy C-Means untuk Klasterisasi Customer Lifetime Value menggunakan Model LRFMD

 (*)Indah Ramadhani Mail (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 M Afdal (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Mustakim Mustakim (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Zarnelly Zarnelly (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: April 2, 2024; Published: July 26, 2024

Abstract

PT X is a retail company engaged in printing. The company has not differentiated between information about profitable and unprofitable customers for the company. Transaction data is only used as profit and loss information so they do not know the characteristics of the customers they have. In addition, the lack of extensive services in the merchandise category is one of the reasons the company's revenue has not reached the predetermined target. Currently, the company has opened additional services in the merchandise field. This research aims to identify customer segmentation as well as analyze the characteristics and provide a strategy proposal that will be submitted to PT. X. Customer loyalty and characteristics have a significant impact on a company. To identify customers who show loyalty to the company, the Fuzzy C-Means algorithm is used to perform clustering, using the Davies Bouldin Index (DBI) to evaluate the clustering results. The model used is in accordance with the principles of Length, Recency, Frequency, Monetary and Diversity (LRFMD) to categorize purchasing patterns. By analyzing LRFMD variables, it is possible to identify customers who are loyal to the company and those who are not. This research produces 6 clusters with the best cluster or supestar customer in cluster 6, the second best value customer or golden customer is cluster 2, the average value customer or typical customer is cluster 4 and 5 and the lowest cluster or dormant customer is in cluster 3.

Keywords


Clustering; Fuzzy C-Means; Customer Characteristics; LRFMD; Segmentation

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