An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry

Ho Thanh (), Nguyen Suong (), Nguyen Huong (), Nguyen Ngoc (), Man Dac-Sang () and Le Thao-Giang ()
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Ho Thanh: University of Economics and Law, Ho Chi Minh City, Vietnam, Vietnam National University, Ho Chi Minh City, Vietnam
Nguyen Suong: University of Economics and Law, Ho Chi Minh City, Vietnam, Vietnam National University, Ho Chi Minh City, Vietnam
Nguyen Huong: University of Economics and Law, Ho Chi Minh City, Vietnam, Vietnam National University, Ho Chi Minh City, Vietnam
Nguyen Ngoc: University of Economics and Law, Ho Chi Minh City, Vietnam, Vietnam National University, Ho Chi Minh City, Vietnam
Man Dac-Sang: University of Economics and Law, Ho Chi Minh City, Vietnam, Vietnam National University, Ho Chi Minh City, Vietnam
Le Thao-Giang: University of Economics and Law, Ho Chi Minh City, Vietnam, Vietnam National University, Ho Chi Minh City, Vietnam

Business Systems Research, 2023, vol. 14, issue 1, 26-53

Abstract: Background Customer segmentation has become one of the most innovative ways which help businesses adopt appropriate marketing campaigns and reach targeted customers. The RFM model and machine learning combination have been widely applied in various areas. Motivations With the rapid increase of transactional data, the RFM model can accurately segment customers and provide deeper insights into customers’ purchasing behaviour. However, the traditional RFM model is limited to 3 variables, Recency, Frequency and Monetary, without revealing segments based on demographic features. Meanwhile, the contribution of demographic characteristics to marketing strategies is extremely important. Methods/Approach The article proposed an extended RFMD model (D-Demographic) with a combination of behavioural and demographic variables. Customer segmentation can be performed effectively using the RFMD model, K-Means, and K-Prototype algorithms. Results The extended model is applied to the retail dataset, and the experimental result shows 5 clusters with different features. The effectiveness of the new model is measured by the Adjusted Rand Index and Adjusted Mutual Information. Furthermore, we use Cohort analysis to analyse customer retention rates and recommend marketing strategies for each segment. Conclusions According to the evaluation, the proposed RMFD model was deployed with stable results created by two clustering algorithms. Businesses can apply this model to deeply understand customer behaviour with their demographics and launch efficient campaigns.

Keywords: Customer segmentation; RFMD model; K-Means; One hot encoding; K-Prototypes; Cohort analysis; machine learning (search for similar items in EconPapers)
JEL-codes: C61 C63 C67 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:bit:bsrysr:v:14:y:2023:i:1:p:26-53:n:3

DOI: 10.2478/bsrj-2023-0002

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