Customer Loyalty Analysis Using Recency,Frequency, Monetary (RFM) and K-means Cluster for Labuan Bajo Souvenirs in Online Store

Authors

  • Supangat Supangat Universitas 17 Agustus 1945, Indonesia
  • Yosevina Mulyani Universitas 17 Agustus 1945, Indonesia
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DOI:

https://doi.org/10.51519/journalisi.v5i1.421

Keywords:

Online Shop, RFM, K-means, Coustomer Segmentation

Abstract

An average Labuan Bajo souvenir shop sells a variety of souvenirs specific to Labuan Bajo. However, the sales process is still manual, with the shop resorting to telephone or WhatsApp communication to connect with customers for placing orders. To increase sales, typical souvenir shops in Labuan Bajo are interested in adopting effective marketing strategies. Consequently, an automated system is necessary to manage customers. The Recency, Frequency, and Monetary Analysis methods are commonly used for assigning values or weights to customers during transactions. These weights are then analyzed and grouped using k-means. Recent data analysis over the last three months reveals that the typical Labuan Bajo souvenir shop has one regular customer, three potential customers, and six regular customers. Testing the system's features showed that it was functioning correctly, and therefore, it can assist the typical Labuan Bajo souvenir shop in streamlining the sales process.

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References

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Published

2023-03-07

Issue

Vol. 5 No. 1 (2023): Journal of Information Systems and Informatics

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Articles

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How to Cite

[1]
S. Supangat and Y. Mulyani, “Customer Loyalty Analysis Using Recency,Frequency, Monetary (RFM) and K-means Cluster for Labuan Bajo Souvenirs in Online Store”, journalisi, vol. 5, no. 1, pp. 285–299, Mar. 2023, doi: 10.51519/journalisi.v5i1.421.