Original scientific paper
https://doi.org/10.2478/bsrj-2023-0002
An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry
Thanh Ho
; University of Economics and Law & Vietnam National University , Ho Chi Minh City
Suong Nguyen
orcid.org/0009-0002-9140-0685
; University of Economics and Law & Vietnam National University , Ho Chi Minh City
Huong Nguyen
orcid.org/0009-0007-8026-8806
; University of Economics and Law & Vietnam National University , Ho Chi Minh City
Ngoc Nguyen
orcid.org/0009-0000-8813-9098
; University of Economics and Law & Vietnam National University , Ho Chi Minh City
Dac-Sang Man
orcid.org/0009-0001-6647-7755
; University of Economics and Law & Vietnam National University , Ho Chi Minh City
Thao-Giang Le
; University of Economics and Law & Vietnam National University, Ho Chi Minh City
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
Hrčak ID:
307893
URI
Publication date:
12.9.2023.
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