A review of data mining methods in RFM-based customer segmentation

Ernawati, E; Baharin, Safiza Suhana Kamal; Kasmin, Fauziah

doi:10.1088/1742-6596/1869/1/012085

Cited by 30 publications

(20 citation statements)

References 20 publications

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“…In recent literature, the dominant approaches are still K-Means and agglomerative clustering with Ward in particular. In their review, Ernawati et al (2021) discuss that clustering is the most applied method in customer segmentation, and within clustering, K-Means and its variants are the most commonly applied methods. In addition, Ghosal et al (2020), in their review, present K-Means and hierarchical algorithms as the most common methods for a market analysis.…”

Section: State-of-the-art Approaches and Methods In Customer Segmenta...mentioning

confidence: 99%

Improving customer segmentation via classification of key accounts as outliers

Spoor

2022

J Market Anal

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Customer segmentation and key account management are important use cases for clustering algorithms. Here, a data set of a Portuguese wholesaler for food and household supplies is used as an exemplary application. To increase the quality of the analysis, a two-stage approach is proposed. First, key accounts are filtered by a density-based outlier detection. Second, a Gaussian Mixture Model (GMM) is applied to cluster smaller customers. This two-stage approach is aligned with the business implications of key accounts as outstanding and very differently behaving customers as well as with the core idea of an ABC analysis. Also, the exclusion of key accounts corresponds to the definition of outliers as the results of a different underlying mechanism. Using this two-stage approach shows better clustering results compared to using a one-stage approach applying only a GMM. Therefore, it is concluded that density-based detection of key accounts followed by a clustering using a GMM is beneficial for customer segmentation within B2B applications.

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Section: State-of-the-art Approaches and Methods In Customer Segmenta...mentioning

confidence: 99%

Improving customer segmentation via classification of key accounts as outliers

Spoor

2022

J Market Anal

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“…In one of the new and most related research items, Ernawati et al [34] assessed the decision-making (DM) methods that collaborated with the RFM model and synthesized them to propose a customer segmentation framework.…”

Section: Segmentation Validationmentioning

confidence: 99%

Application of Data Mining Methods in Grouping Agricultural Product Customers

Chen

Alazzawi

Mavaluru

et al. 2022

Mathematical Problems in Engineering

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The sheer complexity of the factors influencing decision-making has required organizations to use a tool to understand the relationships between data and make various appropriate decisions based on the information obtained. On the other hand, agricultural products need proper planning and decision-making, like any country’s economic pillars. This is while the segmentation of customers and the analysis of their behavior in the manufacturing and distribution industries are of particular importance due to the targeted marketing activities and effective communication with customers. Customer segmentation is done using data mining techniques based on the variables of purchase volume, repeat purchase, and purchase value. This article deals with the grouping of agricultural product customers. Based on this, the K-means clustering method is used based on the Davies–Bouldin index. The results show that grouping customers into three clusters can increase their purchase value and customer lifespan.

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“…Model analisis RFM (Recency, Frequency dan Monetary) dapat melakukan identifikasi terhadap karakteristik yang dimiliki oleh para pelanggan dan dimana Model RFM tersebut dapat dikombinasikan dengan algoritma untuk mendapatkan nilai akurasi yang tinggi terhadap hasil clusterisasi. [11], [12] Peranan data mining yang begitu penting terkhususnya mempermudah bagi pelaku bisnis untuk menentukan segmentasi pelanggan, sudah seharusnya para pelaku bisnis menerapkan data mining untuk dapat terus bertahan ditengah pasar global. Maka dari itu, penelitian ini bertujuan untuk menerapakan proses data mining pada E-Commerce untuk mengetahui segmentasi pelanggan.…”

Section: Pendahuluanunclassified

Penerapan Data Mining Untuk Menentukan Segmentasi Pelanggan Dengan Menggunakan Algoritma K-Means dan Model RFM Pada E-Commerce

Sharyanto

Lestari

2022

Jur. Ris. Kom.

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The growth of technology has a big impact on life, one of which is the use of the internet. In Indonesia, internet usage has increased every year, this has an impact on changes in the buying and selling process carried out by the community. The basic reason caused by these changes is a wider market reach. So it can be said that the presence of the online market is a positive impact caused by the development of these technologies. E-Commerce is a process of buying and selling transactions and also leasing services that are carried out by utilizing internet technology that is already available on certain media. On this basis, business actors in E-Commerce should have their own characteristics that are easily recognized by consumers. It is necessary for business people to segment potential customers. However, the problem of customer segmentation has become a problem in itself for business actors, this is due to the lack of knowledge of how to segment the customers. Data mining is a process that can be done to solve problems. With data mining the process is carried out by processing the dataset and generating new information. In data mining itself, there are several completion processes, namely the K-Means algorithm for the clustering process and the RFM model for segmentation analysis. The results obtained are that there are 4 customer segments. This can be seen from the testing process carried out from K = 2 to K = 5, in the K = 4 test the most optimal BDI value is 0.6788

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A review of data mining methods in RFM-based customer segmentation

Cited by 30 publications

References 20 publications

Improving customer segmentation via classification of key accounts as outliers

Improving customer segmentation via classification of key accounts as outliers

Application of Data Mining Methods in Grouping Agricultural Product Customers

Penerapan Data Mining Untuk Menentukan Segmentasi Pelanggan Dengan Menggunakan Algoritma K-Means dan Model RFM Pada E-Commerce

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