Penerapan Algoritma Fuzzy C-Means Pada Segmentasi Pelanggan B2B dengan Model LRFM

 (*)Aufa Zahrani Putri Mail (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 M Afdal (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Siti Monalisa (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Inggih Permana (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: May 13, 2023; Published: July 31, 2023

Abstract

PT. XYZ is one of the major pharmaceutical industries in Indonesia by marketing its products through B2B (Business to Business) customers. PT. XYZ doesn't understand what customers need. PT. XYZ also implements a cashback system for B2B customers. This study aims to determine customer segmentation, analysis of customer characteristics, firmgration and proposed strategies provided by researchers to PT. XYZ. Loyalty and customer characteristics are very influential on a company. To show which customers are loyal to the company, the Fuzzy C-Means algorithm is used to cluster and the Davies Bouldien Indeks (DBI) is used for the clustering algorithm results. The algorithm used is according to the Length, Recency, Frequency and Monetary (LRFM) model to classify purchasing behavior. It can be seen from the frequency variable which customers are loyal to which companies are not. Then determine the firmography using the attributes of business entity type, customer type, and location. After determining loyal and non-loyal customers, the analysis of customer characteristics is divided into 4 parts, namely the Superstar Segment or the best customer, which is located in cluster 2 where customers in cluster 2 can have a long-term relationship with the company, then the Golden Segment or which has the second highest value (monetary) is located in cluster 4, then the Average Value Segment or the customer who has the average value of all segments is located in cluster 5 and the Dormant Segment or the lowest customer is located in cluster 3 where customer 3 has little relationship with the company.

Keywords


Fuzzy C-Means Algorithm; Clustering; Firmography; Customer Characteristics; LRFM

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