RFM-analysis as a tool for segmentation of high-tech products' consumers

Tsoy, Marina; Shchekoldin, Vladislav

doi:10.1109/apeie.2016.7807074

Cited by 3 publications

(6 citation statements)

References 2 publications

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“…R -the last purchase reviewer -is calculated as the difference between the current date and the date of the last purchase given in the day decision F -the frequency of purchase -the number of purchases during the observation period. M -monetary -the total amount of capital [13]. More specifically, Recency refers to the time interval concerning previous transactions.…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, it takes a useful tool for customer segmentation in the selection of attributes to get better results. RFM (Recency, Frequency and Monetary) models are one of the most appropriate methods for customer description descrip-tions, which consequently will affect increasing sales [13].In recent years, the RFM model has been referring to customer behavioural analysis,based on the level of eligibility, transaction frequency, and monetary background [14]. However, of course it is essential to add Data Mining Techniques in a customer segmentation clustering.…”

Section: Introductionmentioning

confidence: 99%

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Customer Loyality Segmentation on Point of Sale System Using Recency-Frequency-Monetary (RFM) and K-Means

Rizki

Ginasta

Tamrin

et al. 2020

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It is no doubt that the development of the business world has been progressive. Point of sale is one of the many system used as a means of payment in various existing businesses, especially in heterogeneous markets. The activity of transactions between Point of Sale Systems and Customers occur in the business world. Keep in mind also that one of the main factors of business success, is from customers. There is the need of an attractive strategy and certainly it will be to increase the income and assets of a business. To know that, this research will explore the behavior of customer which is based marketing, through RFM Method (Recency, Frequency and Monetary). The case of this study is in Goldfinger Store. It will do segmentation and also use data mining technique to do clustering by using K-Means with result of loyal and potential customer. The results of segmentation using RFM (Recency, Frequency, Monetary) and K-Means methods have produced multiple clusters by dividing them into groups.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Customer Loyality Segmentation on Point of Sale System Using Recency-Frequency-Monetary (RFM) and K-Means

Rizki

Ginasta

Tamrin

et al. 2020

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“…Penelitian dengan model RFM digunakan mendapatkan informasi awal terkait behaviour seorang nasabah. Proses pengelompokan nasabah yang dilakukan dengan model RFM, sering digunakan dalam proses marketing dan juga segmentasi nasabah untuk kebutuhan bisnis [1], [2], [3]. Menurut John R. Miglautsch, nasabah yang dikelompokan dengan menggunakan RFM, dapat diberikan skor dengan membagi nasabah menjadi beberapa bagian berdasarkan nilai yang diurutkan dari nilai recency, frequency dan juga monetary [2].Proses clustering dapat dilakukan dengan mengelompokkan data yang sudah dilakukan pemrosesan model RFM dimana proses clustering digunakan untuk melihat kemiripan antara objek satu dengan yang lain berdasarkan atribut RFM [1], [4], [5].…”

Section: Their Business Values After Doing the Modeling Using Rfm The...unclassified

“…Keterangan: buycomm = Nilai desimal dari persentase komisi pembelian sellcomm = Nilai desimal dari persentase komisi penjualan trxvalue = Total jumlah total pembelian dan penjualan Setelah merubah data tersebut dilakukan perubahan tipe data pada kolom date dimana tipe data sebelumnya merupakan tipe data String dan diubah menjadi tipe data Datetime. Perubahan dilakukan agar dapat dilakukan analisis terkait waktu yang ada di dalam data transaksi tersebut [3].…”

Section: Data Transformationunclassified

“…Pada tahap ini, dilakukan proses segmentasi nasabah berdasarkan data yang sudah diolah menjadi data RFM dimana dilakukan untuk mengetahui segmentasi terkait nasabah dan juga transaksi terakhir (Recency) dan mengetahui pengelompokan nasabah berdasarkan jumlah yang dihasilkan dengan transaksi yang sering dilakukan [2] [3].…”

Section: F Segmentasi Pelanggan Dengan Menggunakan Cluster K-meansunclassified

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Segmentation and Formation of Customer Regression Model Based on Recency, Frequency and Monetary Analysis

Cristover

Toba

Suteja

2022

JuTISI

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During this pandemic, the number of customers of a securities company has increased quite high. This requires securities companies to conduct analysis related to security customer data against transaction data so that the company can find out the segmentation of registered customers and also so that companies can predict the transaction patterns of customers in the company. In processing transaction data, the RFM (Recency, Frequency, Monetary) model can be used as a way to group customers according to their business values. After doing the modeling using RFM, the data is clustered using the K-Means algorithm to find out the segmentation in the RFM model in each group. The RFM model that has been clustered will produce segments based on the RFM group. In this data, a linear regression analysis process is carried out where each group and segment is analyzed and predicted related to variables such as recency, monetary and frequency. The results of data grouping, customer segmentation and also predictions with linear regression can be one of the company's references to make a business decision. From the linear regression process carried out on the RFM attributes, a prediction of the monetary value of the existing recency value is generated and the monetary value of the frequency can also be known with a fairly good error rate.

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