Analisis Loyalitas Pelanggan Berdasarkan Model LRFM Menggunakan Metode K-Means


  • Runi Aulia Putri * Mail Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Muhammad Jazman Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Syaifullah Syaifullah Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Medyantiwi Rahmawita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • (*) Corresponding Author
Keywords: Customer Loyalty; LRFM; K-Means; Customer Segmentation; Beauty Clinic

Abstract

In the era of intense competition in the beauty industry, it is important for companies to understand customer behavior and identify loyal customer segments. Ths study aims to analyze customer loyalty at the Lanona Skincare Beauty clinic using the LRFM (Length, Recency, Frequency, Monetary) model with the K-Means Clustering method. Beauty clinics have not implemented CRM as part of theur business strategy. There is ineffective marketing strategies. Customer transaction data from April to October 2023 was collected and analyzed to determine customer value based on LRFM parameters. The analysis results show that K-Means is effetive in grouping cutomers until the best three clusters are obtained. Cluster 1 with a results of 0,620 is the most loyal customers, cluster 2 with a results of 0,100 is grouped into new inactive customers and cluster 3 with a results of 0,353 is high frequency customers but low revenue contribution. The proposed marketing strategies for each cluster include rewarding an improving communication to maintain customers loyalty. This research provides valuable insights for Lanona Skincare Beauty Clinic in creating a more focused and succesfull marketing plan to increase customer happiness and loyalty.

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Article History
Submitted: 2024-12-29
Published: 2025-03-01
Abstract View: 7 times
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How to Cite
Putri, R., Jazman, M., Syaifullah, S., & Rahmawita, M. (2025). Analisis Loyalitas Pelanggan Berdasarkan Model LRFM Menggunakan Metode K-Means. Building of Informatics, Technology and Science (BITS), 6(4), 2239-2247. https://doi.org/10.47065/bits.v6i4.6565
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