Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model

Puspitasari, Novianti; Widians, Joan Angelina; Setiawan, Noval Bayu

doi:10.14710/jtsiskom.8.2.2020.78-83

Cited by 12 publications

(11 citation statements)

References 12 publications

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“…K-Means is an algorithm that groups objects with the same characteristics into a cluster, which is determined repeatedly by the value of k (Amalia et al, 2021). Data mining process using K-Means is aimed at grouping data into each cluster which corresponds to the center point (centroid) from each cluster used to identify patterns (Puspitasari et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Syahputra¹,

Hutagalung²

2022

SinkrOn

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The accumulation of new student data every year makes searching and processing data difficult, including selecting superior class students according to their talents and abilities. Therefore, the application of the K-Means Clustering data mining method is carried out to support decisions in grouping superior classes. The report card values for each class were used as parameters with a data sample of 80 students and 3 clusters were taken which then resulted in the selection and distribution of superior classes. The purpose of the study was to classify students in the superior class so that they could improve student achievement at SMK Raksana 2 Medan. Results Based on the calculation of the variable distance at the initial centroid with a sample of 80 students and the third iteration, the WCV value is 360.9745 and the BCV value is 7.3575 with a ratio value of 0.0203. Each cluster, namely: Cluster 1 has 43 students including the superior class category. Cluster 2 has 18 students and Cluster 3 has 19 students. Clusters 2 and 3 are included in the regular class category with a total of 37 students. The web-based K-Means application can provide information and solutions needed by schools to classify and determine superior classes so that they can improve student achievement in schools. These results can be used by the school to analyze student achievement and can assist teachers in forming superior classes so as to motivate students to study harder.

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Section: Methodsmentioning

confidence: 99%

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Syahputra¹,

Hutagalung²

2022

SinkrOn

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“…Sehingga pada penelitian berhasil membentuk suatu model segmentasi pelanggan terbaik dengan model RFM dan metode bisecting k-mens dengan tiga kelompok pelanggan yang dihasilkan yaitu Occasional, Typical, dan Gold. Dari penelitian ini juga dapat dilihat bahwa metode RFM merupakan metode yang baik dalam melakukan analisis perilaku pelanggan [28].…”

Section: A Penerapan Rfm Modelunclassified

“…Berdasarkan pada pengertian diatas, maka dapat disimulasikan perhitungan nilai RFM pada analisis data pelanggan berdasarkan pada penelitian [28] sebagai berikut. Jika diasumsikan penelitian dilakukan pada 14 Agustus 2021, maka perhitungan RFM Model dari data transaksi pada Tabel I dapat dilihat pada Tabel II.…”

Section: A Penerapan Rfm Modelunclassified

“…Dari karakteristik RFM tersebut suatu perusahaan dapat meramalkan nilai perilaku atau mengenali pelanggan mereka, sehingga perusahaan dapat mengembangkan promosi, ISSN 1693 -2951 Wicaksana : Literature Review Analisis Perilaku … menemukan pelanggan baru, mempertahankan pelanggan, dan meningkatkan loyalitas pelanggan. Hal tersebut dapat dilakukan melalui operasi CRM yang tentunya didasarkan pada analisis data pelanggan yang diproses dengan teknik data mining [3], [21], [28]. Dan dari penelitian-penelitian sebelumnya juga diketahui bahwa sejauh ini model RFM banyak digunakan untuk segmnetasi pasar/pelanggan.…”

Section: A Penerapan Rfm Modelunclassified

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Literature Review Analisis Perilaku Pelanggan Menggunakan RFM Model

Wicaksana¹,

Swamardika

Hartati³

2022

JTE

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Abstract— Data is an asset for every company, especially Customer Data. Processing of customer data hereinafter can be referred to as Data Mining. The current tight competition in the industry makes companies have to be careful in processing their customer data, one of which is processing customer data for CRM operations. With the aim of recognizing customers by analyzing customer behavior. So this can be an investment for the company. Customer behavior can be predicted using the RFM model. And on this occasion, the author conducts a study on the implementation the RFM Model in data mining in helping companies to better understand their customers. From the results of the review, the RFM model in the 2016-2021 range, it is more combined with data mining techniques, namely the clustering algorithm, where the goal is to group or segment customers. And from the results of the review, the author also formulates research that can be done, namely combining the RFM model with the TOPSIS method.

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“…In an effort to understand customer needs based on customer data [3], companies segment customers using the unique characteristics of each customer [4]. This customer segmentation process is supported by data mining and RFM models [7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of K-Medoids and K-Means Algorithms in Segmenting Customers based on RFM Criteria

Fitrianti Fahrudin,

Rindiyani

2024

E3S Web Conf.

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The company’s approach to customers is important to maintain the company’s profits. Understanding the differences of each customer is very important so that we can understand customer needs based on customer data. Customer Relationship Management (CRM) is considered as a solution to bridge the company and customers. Customer segmentation needs to be done to make it easier for companies to meet customer needs. Data mining and RFM modelling are used for customer segmentation in online retail companies using K-Means and K-Medoids methods. This research compares the performance of both algorithms using Davies Bouldin Index (DBI) and execution time. The results show K-Means is better in cluster validation and execution time. The average DBI value of K-Means is 0.2962 with an execution time of 0.0960 with k=3, while K-Medoids produces a DBI of 0.8942 and an execution time of 2.4295 with k=5. K-Means RFM customer tiers 1-3: Potential Customer/Golden Customer, Lost Customer/ Dormant Customer, and Superstar/Core Customer, 1-5: Champion and Lost. K-Medoids RFM 1-5: Lost, Loyal Customer, Champion, At Risk, and Hibernating, 1-3: Lost Customer/Dormant Customer, Potential Customer/Golden Customer, Superstar/Core Customer, Potential Customer/Golden Customer, and At Risk Customers/Occasional customer.

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Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model

Cited by 12 publications

References 12 publications

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Literature Review Analisis Perilaku Pelanggan Menggunakan RFM Model

Comparison of K-Medoids and K-Means Algorithms in Segmenting Customers based on RFM Criteria

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