Segmentasi Pelanggan Berdasarkan Model LRFM Menggunakan Algoritma K-Means dan Optimasi Klaster Dinamis

Riyan Wahyudi; Achmad Solihin

doi:10.30591/jpit.v10i3.8735

Segmentasi Pelanggan Berdasarkan Model LRFM Menggunakan Algoritma K-Means dan Optimasi Klaster Dinamis

Riyan Wahyudi, Achmad Solihin

Abstract

The number of tax training participants often does not meet the minimum quota, resulting in the cancellation of many training classes. Throughout 2022, there were 27 training classes that failed to take place due to a lack of participants. One of the reasons is that promotions have not utilised historical customer data to set marketing targets more precisely. By utilising historical customer data, companies can design more targeted promotional strategies and increase the number of training participants. Therefore, this research aims to segment customers using the dynamic K-Means algorithm based on the Length, Recency, Frequency, and Monetary (LRFM) model, so that customer behaviour patterns when registering for training can be identified. The clustering results are then visualised to facilitate analysis and decision-making. This research resulted in three customer segments, namely Loyal customers (Gold, 17%), Lost customers (Diamond, 64%), and New customers (Silver, 17%). With this segmentation, it is expected that the company can conduct more effective promotions and increase the number of trainees in the future.

Keywords

Data Mining; Customer Segmentation; K-Means; Model LRFM.

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DOI: https://doi.org/10.30591/jpit.v10i3.8735