Customer segmentation with RFM models and demographic variable using DBSCAN algorithm

Siti Monalisa · Yosie Juniarti · Eki Saputra · Fitriani Muttakin · Tengku Khairil Ahsyar

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Customer churn and segmentation Advanced Clustering Algorithms Research Data Mining and Machine Learning Applications

Abstract

The aims of this research was to identify prospective customers by conducting customer segmentation based on recency, frequency, monetary (RFM) values and demographic variables. The step were selected the data and normalized. The normalized data were clustered using the density based spatial clustering of applications with noise (DBSCAN) algorithm. The k-dist graph was utilized with RStudio tools to identify the best values for epsilon and MinPts. The outcome of utilizing epsilon 0.06 and MinPts 3 was the identification of 5 clusters and 31 data points considered as noise, resulting in a silhouette index (SI) value of 0.4222. Based on the average RFM values, cluster 1 was categorized as prospective customers, while clusters 2, 3, 4, and 5 were designated as loyal customers. Furthermore, according to demographic analysis, the majority of customers are between the ages of 35 to 45, female, married, and housewives. Women, groceries, such as rice and cooking oil, were the most popular products. Besides, the customers were mostly lecturers and lived in Pekanbaru. This was compatible with the customer target of people from upper middle class, such as lecturers, and with the location of the mart as well, which was near a campus.

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References

2008Application of data mining techniques in customer relationship management: A literature review and classification 2008Classifying the segmentation of customer value via RFM model and RS theory 1987Market Segmentation: A Review 2009Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty 2018RFM ranking – An effective approach to customer segmentation 2010A review of the application of RFM model 2016Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis 2023Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews

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